1
|
Tu SJ, Wang SP, Cheng FC, Chen YJ. Extraction of gray-scale intensity distributions from micro computed tomography imaging for femoral cortical bone differentiation between low-magnesium and normal diets in a laboratory mouse model. Sci Rep 2019; 9:8135. [PMID: 31148574 PMCID: PMC6544618 DOI: 10.1038/s41598-019-44610-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 05/13/2019] [Indexed: 02/01/2023] Open
Abstract
Previous studies have shown that the geometric development of femoral trabecular bone is affected by insufficient dietary intake of magnesium. However, it is not clear whether the development of femoral cortical bone can be quantitatively evaluated according to a diet with inadequate magnesium supplementation. Therefore, we used a micro computed tomography (CT) imaging approach with a laboratory mouse model to explore the potential application of texture analysis for the quantitative assessment of femoral cortical bones. C57BL/6J male mice were divided into two groups, where one group was fed a normal diet and the other group was fed a low-magnesium diet. We used a micro CT scanner for image acquisition, and the subsequent development of cortical bone was examined by texture analysis based on the statistical distribution of gray-scale intensities in which seven essential parameters were extracted from the micro CT images. Our calculations showed that the mean intensity increased by 7.20% (p = 0.000134), sigma decreased by 29.18% (p = 1.98E-12), skewness decreased by 19.52% (p = 0.0000205), kurtosis increased by 9.62% (p = 0.0877), energy increased by 24.19% (p = 3.32E-09), entropy decreased by 6.14% (p = 3.00E-10), and the Nakagami parameter increased by 104.32% (p = 4.13E-12) in the low-magnesium group when compared to the normal group. We found that the statistical parameters extracted from the gray-scale intensity distribution were able to differentiate between femoral cortical bone developments in the two different diet groups.
Collapse
Affiliation(s)
- Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan.,Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
| | - Shun-Ping Wang
- Department of Orthopedics, Taichung Veterans General Hospital, Taichung, Taiwan.,College of Science, Tunghai University, Taichung, Taiwan
| | - Fu-Chou Cheng
- Stem Cell Center, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ying-Ju Chen
- Department of Food and Nutrition, Providence University, Taichung, Taiwan.
| |
Collapse
|
2
|
Zhang G, Yang Z, Gong L, Jiang S, Wang L, Cao X, Wei L, Zhang H, Liu Z. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J Med Syst 2019; 43:181. [PMID: 31093830 DOI: 10.1007/s10916-019-1327-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 05/08/2019] [Indexed: 12/17/2022]
Abstract
As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
Collapse
Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Gong
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. .,Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
| | - Lu Wang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Xi Cao
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Lin Wei
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Hongyun Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Ziqi Liu
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| |
Collapse
|
3
|
Wang X, Mao K, Wang L, Yang P, Lu D, He P. An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images. SENSORS (BASEL, SWITZERLAND) 2019; 19:E194. [PMID: 30621101 PMCID: PMC6338921 DOI: 10.3390/s19010194] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 12/28/2018] [Accepted: 12/31/2018] [Indexed: 12/23/2022]
Abstract
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.
Collapse
Affiliation(s)
- Xinqi Wang
- School of Software, Northeastern University, Shenyang 110004, China.
| | - Keming Mao
- School of Software, Northeastern University, Shenyang 110004, China.
| | - Lizhe Wang
- Norman Bethune Health Science Center of Jilin University, No. 2699 Qianjin Street, Changchun 130012, China.
| | - Peiyi Yang
- School of Software, Northeastern University, Shenyang 110004, China.
| | - Duo Lu
- School of Software, Northeastern University, Shenyang 110004, China.
| | - Ping He
- School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China.
| |
Collapse
|
4
|
Digumarthy SR, Padole AM, Gullo RL, Sequist LV, Kalra MK. Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine (Baltimore) 2019; 98:e13963. [PMID: 30608433 PMCID: PMC6344142 DOI: 10.1097/md.0000000000013963] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.
Collapse
Affiliation(s)
| | | | | | - Lecia V. Sequist
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | | |
Collapse
|
5
|
Heinrich MP, Blendowski M, Oktay O. TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions. Int J Comput Assist Radiol Surg 2018; 13:1311-1320. [PMID: 29850978 DOI: 10.1007/s11548-018-1797-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/21/2018] [Indexed: 10/16/2022]
Abstract
PURPOSE Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). METHODS We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. RESULTS We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. CONCLUSIONS We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.
Collapse
Affiliation(s)
- Mattias P Heinrich
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Max Blendowski
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Ozan Oktay
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| |
Collapse
|
6
|
Farag AA, Ali A, Elshazly S, Farag AA. Feature fusion for lung nodule classification. Int J Comput Assist Radiol Surg 2017. [PMID: 28623478 DOI: 10.1007/s11548-017-1626-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning. METHODS Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules. RESULTS A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers. CONCLUSION In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.
Collapse
Affiliation(s)
- Amal A Farag
- Kentucky Imaging Technologies, LLC., Louisville, KY, USA.
| | - Asem Ali
- Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY, 40292, USA
| | - Salwa Elshazly
- Kentucky Imaging Technologies, LLC., Louisville, KY, USA
| | - Aly A Farag
- Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY, 40292, USA
| |
Collapse
|
7
|
Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C, Scholten ET, Schaefer-Prokop C, Wille MMW, Marchianò A, Pastorino U, Prokop M, van Ginneken B. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep 2017; 7:46479. [PMID: 28422152 PMCID: PMC5395959 DOI: 10.1038/srep46479] [Citation(s) in RCA: 180] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 03/17/2017] [Indexed: 12/16/2022] Open
Abstract
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.
Collapse
Affiliation(s)
- Francesco Ciompi
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Kaman Chung
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sarah J van Riel
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Paul K Gerke
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Colin Jacobs
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ernst Th Scholten
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Mathilde M W Wille
- Department of Respiratory Medicine, Gentofte Hospital, Copenhagen, Denmark
| | | | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Mathias Prokop
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| |
Collapse
|
8
|
Ma L, Liu X, Fei B. Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases. Phys Med Biol 2016; 62:612-632. [PMID: 28033116 DOI: 10.1088/1361-6560/62/2/612] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.
Collapse
Affiliation(s)
- Ling Ma
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA. School of Computer Science, Beijing Institute of Technology, Beijing, People's Republic of China
| | | | | |
Collapse
|
9
|
Mao K, Deng Z. Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:1091279. [PMID: 28053650 PMCID: PMC5174747 DOI: 10.1155/2016/1091279] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 11/06/2016] [Indexed: 11/29/2022]
Abstract
This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.
Collapse
Affiliation(s)
- Keming Mao
- College of Software, Northeastern University, Shenyang, Liaoning Province 110004, China
| | - Zhuofu Deng
- College of Software, Northeastern University, Shenyang, Liaoning Province 110004, China
| |
Collapse
|
10
|
Abstract
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
Collapse
|
11
|
Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 2015; 8:2015-22. [PMID: 26346558 PMCID: PMC4531007 DOI: 10.2147/ott.s80733] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.
Collapse
Affiliation(s)
- Kai-Lung Hua
- Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Che-Hao Hsu
- Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shintami Chusnul Hidayati
- Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Wen-Huang Cheng
- Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
| | - Yu-Jen Chen
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan
| |
Collapse
|
12
|
Ng G, Song Y, Cai W, Zhou Y, Liu S, Feng DD. Hierarchical and binary spatial descriptors for lung nodule image retrieval. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:6463-6. [PMID: 25571476 DOI: 10.1109/embc.2014.6945108] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.
Collapse
|
13
|
Song Y, Cai W, Zhou Y, Feng DD. Feature-based image patch approximation for lung tissue classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:797-808. [PMID: 23340591 DOI: 10.1109/tmi.2013.2241448] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.
Collapse
Affiliation(s)
- Yang Song
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney 2006, Australia.
| | | | | | | |
Collapse
|
14
|
Abstract
Heterogeneity is a key feature of malignancy associated with adverse tumour biology. Quantifying heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying tumour biology that CT texture analysis may reflect includes (but is not limited to) tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about tumour characterization, prognosis and treatment prediction and response.
Collapse
Affiliation(s)
- Balaji Ganeshan
- Institute of Nuclear Medicine, University College London, Eustace Road, London, UK.
| | | |
Collapse
|