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Natarajan S, Govindaraj V, Venkata Rao Narayana R, Zhang YD, Murugan PR, Kandasamy K, Ejaz K. A novel triple-level combinational framework for brain anomaly segmentation to augment clinical diagnosis. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2021.1986858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Senthilkumar Natarajan
- Department of ECE, Kalasalingam Academy of Research and Education (Kalasalingam University), Srivilliputtur, India
| | - Vishnuvarthanan Govindaraj
- Department of BME, Kalasalingam Academy of Research and Education (Kalasalingam University), Srivilliputtur, India
| | | | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, UK
| | | | - Karunanithi Kandasamy
- Department of EEE, Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology, Avadi, India
| | - Khurram Ejaz
- Department of CS, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
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2
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Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W. Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol 2021; 32:1634-1643. [PMID: 34505195 DOI: 10.1007/s00330-021-08244-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/21/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, People's Republic of China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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Gao D, Zhang X, Zhou C, Fan W, Zeng T, Yang Q, Yuan J, He Q, Liang D, Liu X, Yang Y, Zheng H, Hu Z. MRI-aided kernel PET image reconstruction method based on texture features. Phys Med Biol 2021; 66. [PMID: 34192685 DOI: 10.1088/1361-6560/ac1024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 06/30/2021] [Indexed: 11/11/2022]
Abstract
We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.
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Affiliation(s)
- Dongfang Gao
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Xu Zhang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, People's Republic of China
| | - Chao Zhou
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, People's Republic of China
| | - Wei Fan
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, People's Republic of China
| | - Tianyi Zeng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Qian Yang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Jianmin Yuan
- Central Research Institute, Shanghai United Imaging Healthcare, Shanghai 201807, People's Republic of China
| | - Qiang He
- Central Research Institute, Shanghai United Imaging Healthcare, Shanghai 201807, People's Republic of China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Xin Liu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Yongfeng Yang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen 518055, People's Republic of China
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Wang PN, Velikina JV, Strigel RM, Henze Bancroft LC, Samsonov AA, Cashen TA, Wang K, Kelcz F, Johnson KM, Korosec FR, Ersoz A, Holmes JH. Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI. Magn Reson Med 2021; 85:3071-3084. [PMID: 33306217 DOI: 10.1002/mrm.28628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI. METHODS Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform. RESULTS The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values. CONCLUSION Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.
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Affiliation(s)
- Ping N Wang
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Julia V Velikina
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ty A Cashen
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Kang Wang
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Frank R Korosec
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ali Ersoz
- MR Engineering, GE Healthcare, Waukesha, Wisconsin, USA
| | - James H Holmes
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
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Çetinel G, Mutlu F, Gül S. Decision support system for breast lesions via dynamic contrast enhanced magnetic resonance imaging. Phys Eng Sci Med 2020; 43:1029-1048. [PMID: 32691326 DOI: 10.1007/s13246-020-00902-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 07/12/2020] [Indexed: 10/23/2022]
Abstract
The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k- nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.
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Affiliation(s)
- Gökçen Çetinel
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey.
| | - Fuldem Mutlu
- Internal Medical Sciences, Radiology Department, Education and Research Hospital, Sakarya University, Sakarya, Turkey
| | - Sevda Gül
- Department of Electronics and Automation, Adapazarı Vocational High School, Sakarya University, Sakarya, Turkey
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Chronaiou I, Giskeødegård GF, Goa PE, Teruel J, Hedayati R, Lundgren S, Huuse EM, Pickles MD, Gibbs P, Sitter B, Bathen TF. Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer. Acta Radiol 2020; 61:875-884. [PMID: 31744303 DOI: 10.1177/0284185119885116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. PURPOSE To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. MATERIAL AND METHODS This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. RESULTS Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan-Meier analysis. CONCLUSION This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.
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Affiliation(s)
- Ioanna Chronaiou
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Guro Fanneløb Giskeødegård
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Jose Teruel
- Department of Radiation Oncology, NYU Langone Health, New York, NY, USA
| | - Roja Hedayati
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Steinar Lundgren
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Else Marie Huuse
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Martin D Pickles
- Radiology Department, Hull University Teaching Hospitals NHS Trust, Hull, UK
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Beathe Sitter
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
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Garpebring A, Brynolfsson P, Kuess P, Georg D, Helbich TH, Nyholm T, Löfstedt T. Density estimation of grey-level co-occurrence matrices for image texture analysis. Phys Med Biol 2018; 63:195017. [PMID: 30088815 DOI: 10.1088/1361-6560/aad8ec] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI). The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features. Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes. The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about [Formula: see text]). In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.
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Guo R, Lu G, Qin B, Fei B. Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:37-70. [PMID: 29107353 PMCID: PMC6169997 DOI: 10.1016/j.ultrasmedbio.2017.09.012] [Citation(s) in RCA: 195] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/12/2017] [Accepted: 09/13/2017] [Indexed: 05/25/2023]
Abstract
Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.
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Affiliation(s)
- Rongrong Guo
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA; Department of Ultrasound, Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi, China
| | - Guolan Lu
- The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA; The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA; Department of Mathematics and Computer Science, Emory College of Emory University, Atlanta, Georgia, USA; Winship Cancer Institute of Emory University, Atlanta, Georgia, USA.
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DSouza AM, Abidin AZ, Leistritz L, Wismüller A. Identifying HIV Associated Neurocognitive Disorder Using Large-Scale Granger Causality Analysis on Resting-State Functional MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10133. [PMID: 29167591 DOI: 10.1117/12.2254690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Lutz Leistritz
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Science, and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Germany
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DSouza AM, Abidin AZ, Wismüller A. Investigating Changes in Resting-State Connectivity from Functional MRI Data in Patients with HIV Associated Neurocognitive Disorder Using MCA and Machine Learning. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10137. [PMID: 29170578 DOI: 10.1117/12.2254189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Infection of the brain by the Human Immunodeficiency Virus (HIV) causes irreversible damage to the synaptic connections resulting in cognitive impairment. Patients with HIV infection, showing signs of impairment in multiple cognitive domains, as assessed by neuropsychological testing, are said to exhibit symptoms of HIV Associated Neurocognitive Disorder (HAND). In this study, we use resting-state functional MRI (fMRI) data to distinguish between healthy subjects and subjects with symptoms of HAND. To this end, we first establish a measure of interaction between pairs of regional time-series by quantifying their non-linear functional connectivity using Mutual Connectivity Analysis (MCA). Subsequently, we use a classifier to distinguish patterns of interaction between healthy and diseased individuals. Our results, quantified as the mean Area under the ROC curve (AUC) over 75 iterations, indicate that, using fMRI data, we can discriminate between the two cohorts well (AUC > 0.8). Specifically, we find that MCA (mean AUC = 0.89) based connectivity features perform significantly better (p < 0.05) when compared to cross-correlation (mean AUC = 0.82) at the classification task. A higher AUC using our approach suggests that such a nonlinear approach is better able to capture connectivity changes between brain regions and has potential for the development of novel neuro-imaging biomarkers.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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Abidin AZ, Jameson J, Molthen R, Wismüller A. Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10134. [PMID: 29187770 DOI: 10.1117/12.2254421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Few studies have analyzed the microstructural properties of bone in cases of Osteogenenis Imperfecta (OI), or 'brittle bone disease'. Current approaches mainly focus on bone mineral density measurements as an indirect indicator of bone strength and quality. It has been shown that bone strength would depend not only on composition but also structural organization. This study aims to characterize 3D structure of the cortical bone in high-resolution micro CT images. A total of 40 bone fragments from 28 subjects (13 with OI and 15 healthy controls) were imaged using micro tomography using a synchrotron light source (SRμCT). Minkowski functionals - volume, surface, curvature, and Euler characteristics - describing the topological organization of the bone were computed from the images. The features were used in a machine learning task to classify between healthy and OI bone. The best classification performance (mean AUC - 0.96) was achieved with a combined 4-dimensional feature of all Minkowski functionals. Individually, the best feature performance was seen using curvature (mean AUC - 0.85), which characterizes the edges within a binary object. These results show that quantitative analysis of cortical bone microstructure, in a computer-aided diagnostics framework, can be used to distinguish between healthy and OI bone with high accuracy.
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Affiliation(s)
- Anas Z Abidin
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, NY, United States
| | - John Jameson
- Dept. of Biomedical Engineering, Marquette University, Milwaukee, WI, USA.,Orthopaedic & Rehabilitation Engineering Center (OREC), Marquette University, Milwaukee, WI, USA
| | - Robert Molthen
- Dept. of Biomedical Engineering, Marquette University, Milwaukee, WI, USA
| | - Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, NY, United States
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Abidin AZ, Chockanathan U, DSouza AM, Inglese M, Wismüller A. Using Large-Scale Granger Causality to Study Changes in Brain Network Properties in the Clinically Isolated Syndrome (CIS) Stage of Multiple Sclerosis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10137:101371B. [PMID: 29167592 PMCID: PMC5695927 DOI: 10.1117/12.2254395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (p<0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant (p<0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.
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Affiliation(s)
- Anas Z. Abidin
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, NY, USA
| | | | - Adora M. DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Axel Wismüller
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, NY, USA
- Department of Biophysics, University of Rochester, NY, USA
- Department of Electrical Engineering, University of Rochester, NY, USA
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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14
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Yin XX, Zhang Y, Cao J, Wu JL, Hadjiloucas S. Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:87-114. [PMID: 28110743 DOI: 10.1016/j.cmpb.2016.08.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 07/23/2016] [Accepted: 08/31/2016] [Indexed: 06/06/2023]
Abstract
We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.
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Affiliation(s)
- X-X Yin
- Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
| | - Y Zhang
- Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia; School of Computer Science, Fudan University, Shanghai, China.
| | - J Cao
- Nanjing University of Finance and Economics school of Computer Science, Nanjing, China
| | - J-L Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
| | - S Hadjiloucas
- School of Biological Sciences and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK.
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15
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DSouza AM, Abidin AZ, Nagarajan MB, Wismüller A. Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788. [PMID: 29170587 DOI: 10.1117/12.2216900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | | | | | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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16
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Abidin AZ, D’Souza AM, Nagarajan MB, Wismüller A. Detecting Altered connectivity patterns in HIV associated neurocognitive impairment using Mutual Connectivity Analysis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788:97880N. [PMID: 29200596 PMCID: PMC5704779 DOI: 10.1117/12.2217315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard pre-processing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.
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Affiliation(s)
| | - Adora M. D’Souza
- Department of Electrical Engineering, University of Rochester Medical Center, NY, USA
| | - Mahesh B. Nagarajan
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Axel Wismüller
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
- Department of Electrical Engineering, University of Rochester Medical Center, NY, USA
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilians University, Munich, Germany
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17
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Abidin AZ, D'Souza AM, Nagarajan MB, Wismüller A. Investigating Changes in Brain Network Properties in HIV-Associated Neurocognitive Disease (HAND) using Mutual Connectivity Analysis (MCA). PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788. [PMID: 29170586 DOI: 10.1117/12.2217317] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.
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Affiliation(s)
- Anas Zainul Abidin
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Adora M D'Souza
- Department of Electrical Engineering, University of Rochester Medical Center, NY, USA
| | - Mahesh B Nagarajan
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Axel Wismüller
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States.,Department of Electrical Engineering, University of Rochester Medical Center, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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18
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DSouza AM, Abidin AZ, Leistritz L, Wismüller A. Large-Scale Granger Causality Analysis on Resting-State Functional MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788. [PMID: 29170585 DOI: 10.1117/12.2217264] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.
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Affiliation(s)
- Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | | | - Lutz Leistritz
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Science, and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Axel Wismüller
- Department of Electrical Engineering, University of Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Germany
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19
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Checefsky WA, Abidin AZ, Nagarajan MB, Bauer JS, Baum T, Wismüller A. Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9785:978508. [PMID: 29367797 PMCID: PMC5777337 DOI: 10.1117/12.2216898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The current clinical standard for measuring Bone Mineral Density (BMD) is dual X-ray absorptiometry, however more recently BMD derived from volumetric quantitative computed tomography has been shown to demonstrate a high association with spinal fracture susceptibility. In this study, we propose a method of fracture risk assessment using structural properties of trabecular bone in spinal vertebrae. Experimental data was acquired via axial multi-detector CT (MDCT) from 12 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. Common image processing methods were used to annotate the trabecular compartment in the vertebral slices creating a circular region of interest (ROI) that excluded cortical bone for each slice. The pixels inside the ROI were converted to values indicative of BMD. High dimensional geometrical features were derived using the scaling index method (SIM) at different radii and scaling factors (SF). The mean BMD values within the ROI were then extracted and used in conjunction with a support vector machine to predict the failure load of the specimens. Prediction performance was measured using the root-mean-square error (RMSE) metric and determined that SIM combined with mean BMD features (RMSE = 0.82 ± 0.37) outperformed MDCT-measured mean BMD (RMSE = 1.11 ± 0.33) (p < 10-4). These results demonstrate that biomechanical strength prediction in vertebrae can be significantly improved through the use of SIM-derived texture features from trabecular bone.
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Affiliation(s)
- Walter A Checefsky
- Department of Electrical and Computer Engineering, University of Rochester, New York, United States
| | - Anas Z Abidin
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Mahesh B Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Jan S Bauer
- Institute for Diagnostic and Interventional Radiology, Technical University of Munich, Germany
| | - Thomas Baum
- Institute for Diagnostic and Interventional Radiology, Technical University of Munich, Germany
| | - Axel Wismüller
- Department of Electrical and Computer Engineering, University of Rochester, New York, United States
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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20
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography. Med Biol Eng Comput 2015; 53:1211-20. [PMID: 26142112 PMCID: PMC4630098 DOI: 10.1007/s11517-015-1340-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Accepted: 06/22/2015] [Indexed: 01/19/2023]
Abstract
Phase-contrast X-ray computed tomography (PCI-CT) has attracted significant interest in recent years for its ability to provide significantly improved image contrast in low absorbing materials such as soft biological tissue. In the research context of cartilage imaging, previous studies have demonstrated the ability of PCI-CT to visualize structural details of human patellar cartilage matrix and capture changes to chondrocyte organization induced by osteoarthritis. This study evaluates the use of geometrical and topological features for volumetric characterization of such chondrocyte patterns in the presence (or absence) of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and topological features derived from Minkowski Functionals were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Our results show that the classification performance of SIM-derived geometrical features (AUC: 0.90 ± 0.09) is significantly better than Minkowski Functionals volume (AUC: 0.54 ± 0.02), surface (AUC: 0.72 ± 0.06), mean breadth (AUC: 0.74 ± 0.06) and Euler characteristic (AUC: 0.78 ± 0.04) (p < 10(-4)). These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as diagnostic imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA.
| | - Paola Coan
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany
- Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany
- European Synchrotron Radiation Facility, 38000, Grenoble, France
| | - Markus B Huber
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - Paul C Diemoz
- Department of Physics, Ludwig Maximilians University, 85748, Munich, Germany
- European Synchrotron Radiation Facility, 38000, Grenoble, France
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, Rochester, NY, USA
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, 80336, Munich, Germany
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21
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Fox MJ, Gibbs P, Pickles MD. Minkowski functionals: An MRI texture analysis tool for determination of the aggressiveness of breast cancer. J Magn Reson Imaging 2015; 43:903-10. [PMID: 26453892 DOI: 10.1002/jmri.25057] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 09/15/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND This work aims to see whether Minkowski Functionals can be used to distinguish between cancer types before chemotherapy treatment has begun, and whether a response to treatment can be predicted by an initial scan alone. METHODS Fat-nulled T1w 3T DCE-MRI scans were taken of 100 cases of biopsy confirmed breast cancer and a series of binary images created on lesion containing slices. Minkowski Functionals were calculated for each binary image and the change in these values as the binary threshold was raised was described using 6(th) order polynomials. These polynomials were used to compare between patient subgroups, for triple negative breast cancer (TNBC) status, chemotherapy response, biopsy grade, nodal status, and lymphovascular invasion status. RESULTS When using Minkowski Functionals statistically significant (P < 0.05) differences were found between TNBC status, biopsy grade, and lymphovascular invasion status subgroups for all methodologies. The analysis performance did not appear to be affected by the number of threshold steps used. Most notably, very strong differences (P ≤ 0.01) were found between TNBC and other intrinsic subtype patients. When analyzed with a binary logistic regression model, an area under the curve value of 0.917 (0.846-0.987, 95% confidence interval) for TNBC classification was found. CONCLUSION The method of texture analysis presented here provides a novel way to characterize tumors, and demonstrates clear differences between cancer groups which are detectable before treatment begins, and can help with treatment planning as a valuable prognosis tool.
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Affiliation(s)
- Michael J Fox
- Centre for Magnetic Resonance Investigations, HYMS at University of Hull, Hull, United Kingdom
| | - Peter Gibbs
- Centre for Magnetic Resonance Investigations, HYMS at University of Hull, Hull, United Kingdom
| | - Martin D Pickles
- Centre for Magnetic Resonance Investigations, HYMS at University of Hull, Hull, United Kingdom
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22
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Abidin AZ, Nagarajan MB, Checefsky WA, Coan P, Diemoz PC, Hobbs SK, Huber MB, Wismüller A. Volumetric Characterization of Human Patellar Cartilage Matrix on Phase Contrast X-Ray Computed Tomography. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9417. [PMID: 28835729 DOI: 10.1117/12.2082084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 ± 0.06) and homogeneity (AUC = 0.82 ± 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.
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Affiliation(s)
- Anas Z Abidin
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Mahesh B Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Walter A Checefsky
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Paola Coan
- Institute of Clinical Radiology, Ludwig Maximilian University Munich, Germany.,Department of Physics, Ludwig Maximilian University Munich, Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Paul C Diemoz
- Department of Physics, Ludwig Maximilian University Munich, Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Susan K Hobbs
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Markus B Huber
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States.,Institute of Clinical Radiology, Ludwig Maximilian University Munich, Germany
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23
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Wang X, Nagarajan MB, Abidin AZ, DSouza A, Hobbs SK, Wismüller A. Investigating the use of mutual information and non-metric clustering for functional connectivity analysis on resting-state functional MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9417:94171N. [PMID: 29200591 PMCID: PMC5704732 DOI: 10.1117/12.2082565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Functional MRI (fMRI) is currently used to investigate structural and functional connectivity in human brain networks. To this end, previous studies have proposed computational methods that involve assumptions that can induce information loss, such as assumed linear coupling of the fMRI signals or requiring dimension reduction. This study presents a new computational framework for investigating the functional connectivity in the brain and recovering network structure while reducing the information loss inherent in previous methods. For this purpose, pair-wise mutual information (MI) was extracted from all pixel time series within the brain on resting-state fMRI data. Non-metric topographic mapping of proximity (TMP) data was subsequently applied to recover network structure from the pair-wise MI analysis. Our computational framework is demonstrated in the task of identifying regions of the primary motor cortex network on resting state fMRI data. For ground truth comparison, we also localized regions of the primary motor cortex associated with hand movement in a task-based fMRI sequence with a finger-tapping stimulus function. The similarity between our pair-wise MI clustering results and the ground truth is evaluated using the dice coefficient. Our results show that non-metric clustering with the TMP algorithm, as performed on pair-wise MI analysis, was able to detect the primary motor cortex network and achieved a dice coefficient of 0.53 in terms of overlap with the ground truth. Thus, we conclude that our computational framework can extract and visualize valuable information concerning the underlying network structure between different regions of the brain in resting state fMRI.
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Affiliation(s)
- Xixi Wang
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Mahesh B. Nagarajan
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
| | - Anas Z. Abidin
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Adora DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Susan K. Hobbs
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
| | - Axel Wismüller
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
- Department of Electrical Engineering, University of Rochester, NY, USA
- Department of Clinical Radiology, Ludwig Maximilian University, Germany
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24
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Nagarajan MB, Checefsky WA, Abidin AZ, Tsai H, Wang X, Hobbs SK, Bauer JS, Baum T, Wismüller A. Characterizing Trabecular Bone structure for Assessing Vertebral Fracture Risk on Volumetric Quantitative Computed Tomography. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9417. [PMID: 29200590 DOI: 10.1117/12.2082059] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature 'correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCT-measured mean BMD (RMSE = 1.11 ± 0.17) (p < 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Walter A Checefsky
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Anas Z Abidin
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Halley Tsai
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Xixi Wang
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Susan K Hobbs
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States
| | - Jan S Bauer
- Institute for Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Institute for Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester, New York, United States.,Institute for Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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25
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Wismüller A, DSouza AM, Abidin AZ, Wang X, Hobbs SK, Nagarajan MB. Functional Connectivity Analysis in Resting State fMRI with Echo-State Networks and Non-Metric Clustering for Network Structure Recovery. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9417:94171M. [PMID: 29151666 PMCID: PMC5693388 DOI: 10.1117/12.2082106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Echo state networks (ESN) are recurrent neural networks where the hidden layer is replaced with a fixed reservoir of neurons. Unlike feed-forward networks, neuron training in ESN is restricted to the output neurons alone thereby providing a computational advantage. We demonstrate the use of such ESNs in our mutual connectivity analysis (MCA) framework for recovering the primary motor cortex network associated with hand movement from resting state functional MRI (fMRI) data. Such a framework consists of two steps - (1) defining a pair-wise affinity matrix between different pixel time series within the brain to characterize network activity and (2) recovering network components from the affinity matrix with non-metric clustering. Here, ESNs are used to evaluate pair-wise cross-estimation performance between pixel time series to create the affinity matrix, which is subsequently subject to non-metric clustering with the Louvain method. For comparison, the ground truth of the motor cortex network structure is established with a task-based fMRI sequence. Overlap between the primary motor cortex network recovered with our model free MCA approach and the ground truth was measured with the Dice coefficient. Our results show that network recovery with our proposed MCA approach is in close agreement with the ground truth. Such network recovery is achieved without requiring low-pass filtering of the time series ensembles prior to analysis, an fMRI preprocessing step that has courted controversy in recent years. Thus, we conclude our MCA framework can allow recovery and visualization of the underlying functionally connected networks in the brain on resting state fMRI.
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Affiliation(s)
- Axel Wismüller
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
- Department of Electrical Engineering, University of Rochester, NY, USA
- Department of Clinical Radiology, Ludwig Maximilian University, Germany
| | - Adora M. DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Anas Z. Abidin
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Xixi Wang
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Susan K. Hobbs
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
| | - Mahesh B. Nagarajan
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
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26
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography. PLoS One 2015; 10:e0117157. [PMID: 25710875 PMCID: PMC4339581 DOI: 10.1371/journal.pone.0117157] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Accepted: 12/18/2014] [Indexed: 11/28/2022] Open
Abstract
Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.
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Affiliation(s)
- Mahesh B. Nagarajan
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
- * E-mail:
| | - Paola Coan
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
- Faculty of Physics, Ludwig Maximilian University, Munich, Germany
- European Synchrotron Radiation Facility, Grenoble, France
| | - Markus B. Huber
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
| | - Paul C. Diemoz
- Faculty of Physics, Ludwig Maximilian University, Munich, Germany
- European Synchrotron Radiation Facility, Grenoble, France
| | - Axel Wismüller
- Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA
- Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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27
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Wismüller A, Abidin AZ, DSouza AM, Wang X, Hobbs SK, Leistritz L, Nagarajan MB. Nonlinear Functional Connectivity Network Recovery in the Human Brain with Mutual Connectivity Analysis (MCA): Convergent Cross-Mapping and Non-Metric Clustering. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9417:94170M. [PMID: 29367796 PMCID: PMC5777339 DOI: 10.1117/12.2082124] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We explore a computational framework for functional connectivity analysis in resting-state functional MRI (fMRI) data acquired from the human brain for recovering the underlying network structure and understanding causality between network components. Termed mutual connectivity analysis (MCA), this framework involves two steps, the first of which is to evaluate the pair-wise cross-prediction performance between fMRI pixel time series within the brain. In a second step, the underlying network structure is subsequently recovered from the affinity matrix using non-metric network clustering approaches, such as the so-called Louvain method. Finally, we use convergent cross-mapping (CCM) to study causality between different network components. We demonstrate our MCA framework in the problem of recovering the motor cortex network associated with hand movement from resting state fMRI data. Results are compared with a ground truth of active motor cortex regions as identified by a task-based fMRI sequence involving a finger-tapping stimulation experiment. Our results regarding causation between regions of the motor cortex revealed a significant directional variability and were not readily interpretable in a consistent manner across subjects. However, our results on whole-slice fMRI analysis demonstrate that MCA-based model-free recovery of regions associated with the primary motor cortex and supplementary motor area are in close agreement with localization of similar regions achieved with a task-based fMRI acquisition. Thus, we conclude that our MCA methodology can extract and visualize valuable information concerning the underlying network structure between different regions of the brain in resting state fMRI.
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Affiliation(s)
- Axel Wismüller
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
- Department of Electrical Engineering, University of Rochester, NY, USA
- Department of Clinical Radiology, Ludwig Maximilian University, Germany
| | - Anas Z Abidin
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Xixi Wang
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | - Susan K Hobbs
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
| | - Lutz Leistritz
- Institute of Medical Statistics, Computer Sciences, and Documentation, Friedrich Schiller University Jena, Germany
| | - Mahesh B Nagarajan
- Department of Imaging Sciences, University of Rochester Medical Center, NY, USA
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28
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Glaser C, Wismüller A. Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features. J Digit Imaging 2014; 27:98-107. [PMID: 24043594 DOI: 10.1007/s10278-013-9634-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.
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Affiliation(s)
- Mahesh B Nagarajan
- Department of Biomedical Engineering, University of Rochester, 430 Elmwood Ave, Rochester, NY, 14627, USA,
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29
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Liu H, Tan T, van Zelst J, Mann R, Karssemeijer N, Platel B. Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound. J Med Imaging (Bellingham) 2014; 1:024501. [PMID: 26158036 DOI: 10.1117/1.jmi.1.2.024501] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 06/22/2014] [Accepted: 06/26/2014] [Indexed: 11/14/2022] Open
Abstract
We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features ([Formula: see text]).
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Affiliation(s)
- Haixia Liu
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands ; University of Nottingham Malaysia Campus , School Of Computer Science, Room BB79, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Tao Tan
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Jan van Zelst
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Ritse Mann
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Nico Karssemeijer
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Bram Platel
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
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30
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Nagarajan MB, De T, Lochmüller EM, Eckstein F, Wismüller A. Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 29170581 DOI: 10.1117/12.2044352] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
| | - Titas De
- Department of Electrical & Computer Engineering, University of Rochester, USA
| | | | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
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31
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Wang X, Nagarajan MB, Conover D, Ning R, O'Connell A, Wismüller A. Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 29170583 DOI: 10.1117/12.2042397] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Cone beam computed tomography (CBCT) has found use in mammography for imaging the entire breast with sufficient spatial resolution at a radiation dose within the range of that of conventional mammography. Recently, enhancement of lesion tissue through the use of contrast agents has been proposed for cone beam CT. This study investigates whether the use of such contrast agents improves the ability of texture features to differentiate lesion texture from healthy tissue on CBCT in an automated manner. For this purpose, 9 lesions were annotated by an experienced radiologist on both regular and contrast-enhanced CBCT images using two-dimensional (2D) square ROIs. These lesions were then segmented, and each pixel within the lesion ROI was assigned a label - lesion or non-lesion, based on the segmentation mask. On both sets of CBCT images, four three-dimensional (3D) Minkowski Functionals were used to characterize the local topology at each pixel. The resulting feature vectors were then used in a machine learning task involving support vector regression with a linear kernel (SVRlin) to classify each pixel as belonging to the lesion or non-lesion region of the ROI. Classification performance was assessed using the area under the receiver-operating characteristic (ROC) curve (AUC). Minkowski Functionals derived from contrast-enhanced CBCT images were found to exhibit significantly better performance at distinguishing between lesion and non-lesion areas within the ROI when compared to those extracted from CBCT images without contrast enhancement (p < 0.05). Thus, contrast enhancement in CBCT can improve the ability of texture features to distinguish lesions from surrounding healthy tissue.
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Affiliation(s)
- Xixi Wang
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | | | - David Conover
- Department of Imaging Sciences, University of Rochester, NY, USA.,Koning Corporation, Rochester, NY, USA
| | - Ruola Ning
- Department of Imaging Sciences, University of Rochester, NY, USA.,Koning Corporation, Rochester, NY, USA
| | - Avice O'Connell
- Department of Imaging Sciences, University of Rochester, NY, USA
| | - Axel Wismüller
- Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA
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32
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Yang CC, Nagarajan MB, Huber MB, Carballido-Gamio J, Bauer JS, Baum T, Eckstein F, Lochmüller EM, Link TM, Wismüller A. Predicting the Biomechanical Strength of Proximal Femur Specimens with Minkowski Functionals and Support Vector Regression. ACTA ACUST UNITED AC 2014; 9038. [PMID: 29170582 DOI: 10.1117/12.2041782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multi-regression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.
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Affiliation(s)
- Chien-Chun Yang
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Mahesh B Nagarajan
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Markus B Huber
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
| | - Julio Carballido-Gamio
- Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States
| | - Jan S Bauer
- Institute of Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Institute of Diagnostic Radiology, Technical University of Munich, Munich, Germany
| | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University Salzburg, Salzburg, Austria
| | | | - Thomas M Link
- Musculoskeletal and Quantitative Imaging Research, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States
| | - Axel Wismüller
- Departments of Imaging Sciences & Biomedical Engineering, University of Rochester, New York, United States
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33
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Wismüller A. Phase contrast imaging X-ray computed tomography: Quantitative characterization of human patellar cartilage matrix with topological and geometrical features. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9038. [PMID: 28835728 DOI: 10.1117/12.2042395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.
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Affiliation(s)
- Mahesh B Nagarajan
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States
| | - Paola Coan
- Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany.,Faculty of Physics, Ludwig Maximilians University, Munich 85748 Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Markus B Huber
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States
| | - Paul C Diemoz
- Faculty of Physics, Ludwig Maximilians University, Munich 85748 Germany.,European Synchrotron Radiation Facility, Grenoble, France
| | - Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, New York, United States.,Faculty of Medicine & Institute of Clinical Radiology, Ludwig Maximilians University, Munich Germany
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Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology. Artif Intell Med 2013; 60:65-77. [PMID: 24355697 DOI: 10.1016/j.artmed.2013.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2012] [Revised: 08/15/2013] [Accepted: 11/15/2013] [Indexed: 10/26/2022]
Abstract
OBJECTIVE While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. METHODS AND MATERIALS We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. RESULTS Of the feature vectors investigated, the best performance was observed with Minkowski functional 'perimeter' while comparable performance was observed with 'area'. Of the dimension reduction algorithms tested with 'perimeter', the best performance was observed with Sammon's mapping (0.84±0.10) while comparable performance was achieved with exploratory observation machine (0.82±0.09) and principal component analysis (0.80±0.10). CONCLUSIONS The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction.
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35
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Nagarajan MB, Coan P, Huber MB, Diemoz PC, Glaser C, Wismuller A. Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage. IEEE Trans Biomed Eng 2013; 60:2896-903. [PMID: 23744660 DOI: 10.1109/tbme.2013.2266325] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.
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36
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Wismüller A, De T, Lochmüller E, Eckstein F, Nagarajan MB. Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis in Biomedical Imaging. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2013; 8672:86720I. [PMID: 29170580 PMCID: PMC5697726 DOI: 10.1117/12.2007192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4 ). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.
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Affiliation(s)
- Axel Wismüller
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
| | - Titas De
- Department of Electrical & Computer Engineering, University of Rochester, USA
| | - Eva Lochmüller
- Institute of Anatomy and Musculoskeletal Research, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Felix Eckstein
- Institute of Anatomy and Musculoskeletal Research, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Mahesh B. Nagarajan
- Departments of Biomedical Engineering & Imaging Sciences, University of Rochester, USA
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