101
|
Turkki R, Linder N, Kovanen PE, Pellinen T, Lundin J. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform 2016; 7:38. [PMID: 27688929 PMCID: PMC5027738 DOI: 10.4103/2153-3539.189703] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 07/01/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. METHODS Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. RESULTS In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). CONCLUSIONS Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.
Collapse
Affiliation(s)
- Riku Turkki
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Nina Linder
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Panu E. Kovanen
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland
| | - Teijo Pellinen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Johan Lundin
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Department of Public Health Sciences/Global Health (IHCAR), Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
102
|
González E, Bianconi F, Fernández A. An investigation on the use of local multi-resolution patterns for image classification. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.04.044] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
103
|
Deep G, Kaur L, Gupta S. Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1193447] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- G. Deep
- Department of CSE, IET Bhaddal, Punjab Technical University, Ropar, India
| | - L. Kaur
- Department of CE, Punjabi University(Pb.), Patiala, India
| | - S. Gupta
- Department of CSE, UIET, PU, Chandigarh, India
| |
Collapse
|
104
|
Oliver A, Tortajada M, Lladó X, Freixenet J, Ganau S, Tortajada L, Vilagran M, Sentís M, Martí R. Breast Density Analysis Using an Automatic Density Segmentation Algorithm. J Digit Imaging 2016; 28:604-12. [PMID: 25720749 DOI: 10.1007/s10278-015-9777-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density.
Collapse
Affiliation(s)
- Arnau Oliver
- Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain.
| | - Meritxell Tortajada
- Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain
- UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain
| | - Xavier Lladó
- Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain
| | - Jordi Freixenet
- Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain
| | - Sergi Ganau
- UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain
| | - Lidia Tortajada
- UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain
| | - Mariona Vilagran
- UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain
| | - Melcior Sentís
- UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208, Sabadell, Spain
| | - Robert Martí
- Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain
| |
Collapse
|
105
|
Multilayer descriptors for medical image classification. Comput Biol Med 2016; 72:239-47. [DOI: 10.1016/j.compbiomed.2015.11.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Revised: 11/18/2015] [Accepted: 11/19/2015] [Indexed: 11/23/2022]
|
106
|
Hong X, Zhao G, Zafeiriou S, Pantic M, Pietikäinen M. Capturing correlations of local features for image representation. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.07.134] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
107
|
Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M. Median Robust Extended Local Binary Pattern for Texture Classification. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:1368-1381. [PMID: 26829791 DOI: 10.1109/tip.2016.2522378] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.
Collapse
|
108
|
Nanni L, Paci M, Caetano dos Santos FL, Skottman H, Juuti-Uusitalo K, Hyttinen J. Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium. PLoS One 2016; 11:e0149399. [PMID: 26895509 PMCID: PMC4760937 DOI: 10.1371/journal.pone.0149399] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 02/01/2016] [Indexed: 12/02/2022] Open
Abstract
Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f.
Collapse
Affiliation(s)
- Loris Nanni
- Department of Information Engineering, University of Padua, Padua, Italy
- * E-mail: (LN); (MP)
| | - Michelangelo Paci
- Department of Electronics and Communications Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland
- * E-mail: (LN); (MP)
| | | | - Heli Skottman
- University of Tampere, BioMediTech, Tampere, Finland
| | | | - Jari Hyttinen
- Department of Electronics and Communications Engineering, Tampere University of Technology, BioMediTech, Tampere, Finland
| |
Collapse
|
109
|
Guo Z, Wang X, Zhou J, You J. Robust Texture Image Representation by Scale Selective Local Binary Patterns. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:687-99. [PMID: 26685235 DOI: 10.1109/tip.2015.2507408] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition applications, such as texture recognition. It could effectively address grayscale and rotation variation. However, it failed to get desirable performance for texture classification with scale transformation. In this paper, a new method based on dominant LBP in scale space is proposed to address scale variation for texture classification. First, a scale space of a texture image is derived by a Gaussian filter. Then, a histogram of pre-learned dominant LBPs is built for each image in the scale space. Finally, for each pattern, the maximal frequency among different scales is considered as the scale invariant feature. Extensive experiments on five public texture databases (University of Illinois at Urbana-Champaign, Columbia Utrecht Database, Kungliga Tekniska Högskolan-Textures under varying Illumination, Pose and Scale, University of Maryland, and Amsterdam Library of Textures) validate the efficiency of the proposed feature extraction scheme. Coupled with the nearest subspace classifier, the proposed method could yield competitive results, which are 99.36%, 99.51%, 99.39%, 99.46%, and 99.71% for UIUC, CUReT, KTH-TIPS, UMD, and ALOT, respectively. Meanwhile, the proposed method inherits simple and efficient merits of LBP, for example, it could extract scale-robust feature for a 200×200 image within 0.24 s, which is applicable for many real-time applications.
Collapse
|
110
|
Nguyen TP, Vu NS, Manzanera A. Statistical binary patterns for rotational invariant texture classification. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.029] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
111
|
Texture Features for the Detection of Acute Lymphoblastic Leukemia. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2016. [DOI: 10.1007/978-981-10-0135-2_52] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
112
|
Gender and texture classification: A comparative analysis using 13 variants of local binary patterns. Pattern Recognit Lett 2015. [DOI: 10.1016/j.patrec.2015.04.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
113
|
|
114
|
Bianconi F, González E, Fernández A. Dominant local binary patterns for texture classification: Labelled or unlabelled? Pattern Recognit Lett 2015. [DOI: 10.1016/j.patrec.2015.06.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
115
|
Shao W, Liu M, Zhang D. Human cell structure-driven model construction for predicting protein subcellular location from biological images. Bioinformatics 2015; 32:114-21. [PMID: 26363175 DOI: 10.1093/bioinformatics/btv521] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 08/31/2015] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. RESULTS We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. AVAILABILITY AND IMPLEMENTATION The dataset and code can be downloaded from https://github.com/shaoweinuaa/. CONTACT dqzhang@nuaa.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Wei Shao
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Mingxia Liu
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Daoqiang Zhang
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| |
Collapse
|
116
|
Farokhi S, Sheikh UU, Flusser J, Yang B. Near infrared face recognition using Zernike moments and Hermite kernels. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.04.030] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
117
|
Kaya Y, Ertuğrul ÖF, Tekin R. Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
118
|
Wang S, Cong Y, Cao J, Yang Y, Tang Y, Zhao H, Yu H. Scalable gastroscopic video summarization via similar-inhibition dictionary selection. Artif Intell Med 2015; 66:1-13. [PMID: 26363682 DOI: 10.1016/j.artmed.2015.08.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Revised: 06/19/2015] [Accepted: 08/07/2015] [Indexed: 12/22/2022]
Abstract
OBJECTIVE This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video. METHODS AND MATERIALS To select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity. RESULTS For experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency. CONCLUSIONS For gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer-aided diagnosis or medical report generation.
Collapse
Affiliation(s)
- Shuai Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
| | - Yang Cong
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China
| | - Jun Cao
- Department of Computer Science, Arizona State University, 1711 South Rural Road, Tempe, AZ 85287, USA
| | - Yunsheng Yang
- Department of Gastroenterology and Hepatology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100000, China
| | - Yandong Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China
| | - Huaici Zhao
- Key Laboratory of Image Understanding and Computer Vision, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China
| | - Haibin Yu
- Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, China
| |
Collapse
|
119
|
Kotu LP, Engan K, Borhani R, Katsaggelos AK, Ørn S, Woie L, Eftestøl T. Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients. Artif Intell Med 2015; 64:205-15. [PMID: 26239472 DOI: 10.1016/j.artmed.2015.06.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Revised: 06/08/2015] [Accepted: 06/25/2015] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion; a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk. METHODS In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest. RESULTS In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit. CONCLUSION These promising results suggest that the discriminative features introduced in this paper can be used by medical professionals, or in automatic decision support systems, along with the recognized risk markers, to improve arrhythmic risk stratification in post-MI patients.
Collapse
Affiliation(s)
- Lasya Priya Kotu
- Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway.
| | - Kjersti Engan
- Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway.
| | - Reza Borhani
- Department of Electrical Engineering and Computer Science, Northwestern University, 633 Clark St, Evanston, IL 60208, USA
| | - Aggelos K Katsaggelos
- Department of Electrical Engineering and Computer Science, Northwestern University, 633 Clark St, Evanston, IL 60208, USA
| | - Stein Ørn
- Department of Cardiology, Stavanger University Hospital, Gerd Ragna Bloch Thorsens Gate 8, Stavanger 4011, Norway
| | - Leik Woie
- Department of Cardiology, Stavanger University Hospital, Gerd Ragna Bloch Thorsens Gate 8, Stavanger 4011, Norway
| | - Trygve Eftestøl
- Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway
| |
Collapse
|
120
|
Kwak JT, Xu S, Wood BJ. Efficient Data Mining for Local Binary Pattern in Texture Image Analysis. EXPERT SYSTEMS WITH APPLICATIONS 2015; 42:4529-4539. [PMID: 25767332 PMCID: PMC4353407 DOI: 10.1016/j.eswa.2015.01.055] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the grey levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images.
Collapse
Affiliation(s)
- Jin Tae Kwak
- Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda MD 20892, USA
| | - Sheng Xu
- Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda MD 20892, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda MD 20892, USA
| |
Collapse
|
121
|
Berdouses ED, Koutsouri GD, Tripoliti EE, Matsopoulos GK, Oulis CJ, Fotiadis DI. A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. Comput Biol Med 2015; 62:119-35. [PMID: 25932969 DOI: 10.1016/j.compbiomed.2015.04.016] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 03/20/2015] [Accepted: 04/12/2015] [Indexed: 12/01/2022]
Abstract
The aim of this work is to present a computer-aided automated methodology for the assessment of carious lesions, according to the International Caries Detection and Assessment System (ICDAS II), which are located on the occlusal surfaces of posterior permanent teeth from photographic color tooth images. The proposed methodology consists of two stages: (a) the detection of regions of interest and (b) the classification of the detected regions according to ICDAS ΙΙ. In the first stage, pre-processing, segmentation and post-processing mechanisms were employed. For each pixel of the detected regions, a 15×15 neighborhood is used and a set of intensity-based and texture-based features were extracted. A correlation based technique was applied to select a subset of 36 features which were given as input into the classification stage, where five classifiers (J48, Random Tree, Random Forests, Support Vector Machines and Naïve Bayes) were compared to conclude to the best one, in our case, to Random Forests. The methodology was evaluated on a set of 103 digital color images where 425 regions of interest from occlusal surfaces of extracted permanent teeth were manually segmented and classified, based on visual assessments by two experts. The methodology correctly detected 337 out of 340 regions in the detection stage with accuracy of detection 80%. For the classification stage an overall accuracy 83% is achieved. The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge.
Collapse
Affiliation(s)
- Elias D Berdouses
- Department of Paediatric Dentistry, Dental School, National and Kapodistrian University of Athens, GR 11527, Athens, Greece.
| | - Georgia D Koutsouri
- Department of Electrical and Computer Engineering, National Technical University of Athens, GR 15780, Athens, Greece.
| | - Evanthia E Tripoliti
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece.
| | - George K Matsopoulos
- Department of Electrical and Computer Engineering, National Technical University of Athens, GR 15780, Athens, Greece.
| | - Constantine J Oulis
- Department of Paediatric Dentistry, Dental School, National and Kapodistrian University of Athens, GR 11527, Athens, Greece.
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece.
| |
Collapse
|
122
|
|
123
|
Coelho LP, Pato C, Friães A, Neumann A, von Köckritz-Blickwede M, Ramirez M, Carriço JA. Automatic determination of NET (neutrophil extracellular traps) coverage in fluorescent microscopy images. Bioinformatics 2015; 31:2364-70. [PMID: 25792554 DOI: 10.1093/bioinformatics/btv156] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 02/16/2015] [Indexed: 01/07/2023] Open
Abstract
MOTIVATION Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases. RESULTS We propose a framework for the automated determination of NET content, based on visually annotated images which are used to train a supervised machine-learning method. We derive several methods in this framework. The best results are obtained by combining these into a single prediction. The overall Q(2) of the combined method is 93%. By having two experts label part of the image set, we were able to compare the performance of the algorithms to the human interoperator variability. We find that the two operators exhibited a very high correlation on their overall assessment of the NET coverage area in the images (R(2) is 97%), although there were consistent differences in labeling at pixel level (Q(2), which unlike R(2) does not correct for additive and multiplicative biases, was only 89%). AVAILABILITY AND IMPLEMENTATION Open source software (under the MIT license) is available at https://github.com/luispedro/Coelho2015_NetsDetermination for both reproducibility and application to new data.
Collapse
Affiliation(s)
- Luis Pedro Coelho
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| | - Catarina Pato
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| | - Ana Friães
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| | - Ariane Neumann
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| | | | - Mário Ramirez
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| | - João André Carriço
- Unidade de Biofísica e Expressão Genética, Instituto de Medicina Molecular and
| |
Collapse
|
124
|
Improved local ternary patterns for automatic target recognition in infrared imagery. SENSORS 2015; 15:6399-418. [PMID: 25785311 PMCID: PMC4435149 DOI: 10.3390/s150306399] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 12/25/2014] [Accepted: 02/16/2015] [Indexed: 11/17/2022]
Abstract
This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP. Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency. Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods.
Collapse
|
125
|
Werghi N, Berretti S, del Bimbo A. The mesh-LBP: a framework for extracting local binary patterns from discrete manifolds. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:220-235. [PMID: 25398180 DOI: 10.1109/tip.2014.2370253] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this paper, we present a novel and original framework, which we dubbed mesh-local binary pattern (LBP), for computing local binary-like-patterns on a triangular-mesh manifold. This framework can be adapted to all the LBP variants employed in 2D image analysis. As such, it allows extending the related techniques to mesh surfaces. After describing the foundations, the construction and the main features of the mesh-LBP, we derive its possible variants and show how they can extend most of the 2D-LBP variants to the mesh manifold. In the experiments, we give evidence of the presence of the uniformity aspect in the mesh-LBP, similar to the one noticed in the 2D-LBP. We also report repeatability experiments that confirm, in particular, the rotation-invariance of mesh-LBP descriptors. Furthermore, we analyze the potential of mesh-LBP for the task of 3D texture classification of triangular-mesh surfaces collected from public data sets. Comparison with state-of-the-art surface descriptors, as well as with 2D-LBP counterparts applied on depth images, also evidences the effectiveness of the proposed framework. Finally, we illustrate the robustness of the mesh-LBP with respect to the class of mesh irregularity typical to 3D surface-digitizer scans.
Collapse
|
126
|
|
127
|
Yang F, Xu YY, Shen HB. Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification? ScientificWorldJournal 2014; 2014:429049. [PMID: 25050396 PMCID: PMC4094881 DOI: 10.1155/2014/429049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Accepted: 05/22/2014] [Indexed: 01/14/2023] Open
Abstract
Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.
Collapse
Affiliation(s)
- Fan Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Optic-Electronic and Communication, Jiangxi Science & Technology Normal University, Nanchang 330013, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Ying-Ying Xu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| |
Collapse
|
128
|
Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images. Artif Intell Med 2014; 61:105-18. [DOI: 10.1016/j.artmed.2014.05.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 05/14/2014] [Accepted: 05/16/2014] [Indexed: 11/20/2022]
|
129
|
Hong X, Zhao G, Pietikainen M, Chen X. Combining LBP difference and feature correlation for texture description. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2014; 23:2557-2568. [PMID: 24733014 DOI: 10.1109/tip.2014.2316640] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Effective characterization of texture images requires exploiting multiple visual cues from the image appearance. The local binary pattern (LBP) and its variants achieve great success in texture description. However, because the LBP(-like) feature is an index of discrete patterns rather than a numerical feature, it is difficult to combine the LBP(-like) feature with other discriminative ones by a compact descriptor. To overcome the problem derived from the nonnumerical constraint of the LBP, this paper proposes a numerical variant accordingly, named the LBP difference (LBPD). The LBPD characterizes the extent to which one LBP varies from the average local structure of an image region of interest. It is simple, rotation invariant, and computationally efficient. To achieve enhanced performance, we combine the LBPD with other discriminative cues by a covariance matrix. The proposed descriptor, termed the covariance and LBPD descriptor (COV-LBPD), is able to capture the intrinsic correlation between the LBPD and other features in a compact manner. Experimental results show that the COV-LBPD achieves promising results on publicly available data sets.
Collapse
|
130
|
Yang F, Xu YY, Wang ST, Shen HB. Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.10.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
131
|
Altınçay H, Erenel Z. Ternary encoding based feature extraction for binary text classification. APPL INTELL 2014. [DOI: 10.1007/s10489-014-0515-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
132
|
Athanasiou LS, Bourantas CV, Rigas G, Sakellarios AI, Exarchos TP, Siogkas PK, Ricciardi A, Naka KK, Papafaklis MI, Michalis LK, Prati F, Fotiadis DI. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images. JOURNAL OF BIOMEDICAL OPTICS 2014; 19:026009. [PMID: 24525828 DOI: 10.1117/1.jbo.19.2.026009] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 01/17/2014] [Indexed: 05/23/2023]
Abstract
Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson's correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts' annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.
Collapse
Affiliation(s)
- Lambros S Athanasiou
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, Greece
| | - Christos V Bourantas
- ThoraxCenter, Erasmus Medical Center, 's-Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands
| | - George Rigas
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, GreececFORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, G
| | - Antonis I Sakellarios
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, Greece
| | - Themis P Exarchos
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, GreececFORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, G
| | - Panagiotis K Siogkas
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, Greece
| | - Andrea Ricciardi
- Ettore Sansavini Health Science Foundation, 48100 Ravenna, Italy
| | - Katerina K Naka
- University of Ioannina, Medical School, Michaelidion Cardiac Center and Department of Cardiology, GR 45110 Ioannina, Greece
| | - Michail I Papafaklis
- Brigham and Women's Hospital, Harvard Medical School Cardiovascular Division, Boston, Massachusetts 02115
| | - Lampros K Michalis
- University of Ioannina, Medical School, Michaelidion Cardiac Center and Department of Cardiology, GR 45110 Ioannina, Greece
| | - Francesco Prati
- San Giovanni Hospital, Interventional Cardiology, Via dell'Amba Aradam 8, 00184 Rome, Italy
| | - Dimitrios I Fotiadis
- University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, GreececFORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, G
| |
Collapse
|
133
|
A Unifying Framework for LBP and Related Methods. LOCAL BINARY PATTERNS: NEW VARIANTS AND APPLICATIONS 2014. [DOI: 10.1007/978-3-642-39289-4_2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
|
134
|
Zhang L, Kong H, Ting Chin C, Liu S, Fan X, Wang T, Chen S. Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry A 2013; 85:214-30. [PMID: 24376056 DOI: 10.1002/cyto.a.22407] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 09/27/2013] [Accepted: 10/05/2013] [Indexed: 11/08/2022]
Abstract
Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals.
Collapse
Affiliation(s)
- Ling Zhang
- Department of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen, 518060, China; Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound Imaging, Shenzhen, 518060, China
| | | | | | | | | | | | | |
Collapse
|
135
|
Yang B, Chen S. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.10.032] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
136
|
Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.06.027] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
137
|
Ren J, Jiang X, Yuan J. Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:4049-4060. [PMID: 23797250 DOI: 10.1109/tip.2013.2268976] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem. Both LBP and LTP, however, treat the corrupted image patterns as they are. In view of this, we propose a noise-resistant LBP (NRLBP) to preserve the image local structures in presence of noise. The small pixel difference is vulnerable to noise. Thus, we encode it as an uncertain state first, and then determine its value based on the other bits of the LBP code. It is widely accepted that most of the image local structures are represented by uniform codes and noise patterns most likely fall into the non-uniform codes. Therefore, we assign the value of an uncertain bit hence as to form possible uniform codes. Thus, we develop an error-correction mechanism to recover the distorted image patterns. In addition, we find that some image patterns such as lines are not captured in uniform codes. Those line patterns may appear less frequently than uniform codes, but they represent a set of important local primitives for pattern recognition. Thus, we propose an extended noise-resistant LBP (ENRLBP) to capture line patterns. The proposed NRLBP and ENRLBP are more resistant to noise compared with LBP, LTP, and many other variants. On various applications, the proposed NRLBP and ENRLBP demonstrate superior performance to LBP/LTP variants.
Collapse
Affiliation(s)
- Jianfeng Ren
- BeingThere Centre, Institute of Media Innovation, Nanyang Technological University, Singapore.
| | | | | |
Collapse
|
138
|
Brahnam S, Jain LC, Lumini A, Nanni L. Introduction to Local Binary Patterns: New Variants and Applications. ACTA ACUST UNITED AC 2013. [DOI: 10.1007/978-3-642-39289-4_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
|
139
|
Xu YY, Yang F, Zhang Y, Shen HB. An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues. ACTA ACUST UNITED AC 2013; 29:2032-40. [PMID: 23740749 DOI: 10.1093/bioinformatics/btt320] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
MOTIVATION Human cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer. RESULTS To reveal the cancer-related protein mislocalizations, we developed an image-based multi-label subcellular location predictor, iLocator, which covers seven cellular localizations. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins. We first trained and tested iLocator on 3240 normal human tissue images that have known subcellular location information from the human protein atlas. The iLocator was then used to generate protein localization predictions for 3696 protein images from seven cancer tissues that have no location annotations in the human protein atlas. By comparing the output data from normal and cancer tissues, we detected eight potential cancer biomarker proteins that have significant localization differences with P-value < 0.01. AVAILABILITY http://www.csbio.sjtu.edu.cn/bioinf/iLocator/
Collapse
Affiliation(s)
- Ying-Ying Xu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | | | | | | |
Collapse
|
140
|
|
141
|
|
142
|
|
143
|
Abstract
Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.
Collapse
Affiliation(s)
- Xianjing Meng
- School of Computer Science and Technology, Shandong University, Jinan 250101, China.
| | | | | | | |
Collapse
|
144
|
Wing-Yin Chan, Jing Qin, Yim-Pan Chui, Pheng-Ann Heng. A Serious Game for Learning Ultrasound-Guided Needle Placement Skills. ACTA ACUST UNITED AC 2012; 16:1032-42. [DOI: 10.1109/titb.2012.2204406] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
145
|
Athanasiou LS, Exarchos TP, Naka KK, Michalis LK, Prati F, Fotiadis DI. Atherosclerotic plaque characterization in Optical Coherence Tomography images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:4485-8. [PMID: 22255335 DOI: 10.1109/iembs.2011.6091112] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Optical Coherence Tomography (OCT) is a fiber--optic imaging modality which produces high resolution tomographic images of the coronary lumen and outer vessel wall. While OCT images present morphological information in highly resolved detail, the characterization of the various plaque components relies on trained readers. The aim of this study is to extract a set of features in grayscale OCT images and to use them in order to classify the atherosclerotic plaque. Intensity and texture based features we used in order to classify the plaque in four plaque types: Calcium (C), Lipid Pool (LP), Fibrous Tissue (FT) and Mixed Plaque (MP). 50 OCT annotated images from 3 patients were used to train and test the proposed plaque characterization method. Using a Random Forests classifier overall classification accuracy 80.41% is reported.
Collapse
Affiliation(s)
- L S Athanasiou
- Unit of Medical Technology and Intelligent Information Systems, Dept of Materials Science and Engineering, University of Ioannina, GR 45110.
| | | | | | | | | | | |
Collapse
|
146
|
Nanni L, Brahnam S, Lumini A. Combining multiple approaches for gene microarray classification. Bioinformatics 2012; 28:1151-7. [DOI: 10.1093/bioinformatics/bts108] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
147
|
Linder N, Konsti J, Turkki R, Rahtu E, Lundin M, Nordling S, Haglund C, Ahonen T, Pietikäinen M, Lundin J. Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn Pathol 2012; 7:22. [PMID: 22385523 PMCID: PMC3315400 DOI: 10.1186/1746-1596-7-22] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Accepted: 03/02/2012] [Indexed: 11/22/2022] Open
Abstract
Background The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images. Results The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, P < 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively. Conclusions The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537
Collapse
Affiliation(s)
- Nina Linder
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | | | | | | | | | | | | | | | | | | |
Collapse
|
148
|
|
149
|
Athanasiou LS, Karvelis PS, Tsakanikas VD, Naka KK, Michalis LK, Bourantas CV, Fotiadis DI. A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology. ACTA ACUST UNITED AC 2011; 16:391-400. [PMID: 22203721 DOI: 10.1109/titb.2011.2181529] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated in the clinical and research arena.
Collapse
Affiliation(s)
- Lambros S Athanasiou
- Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
| | | | | | | | | | | | | |
Collapse
|
150
|
|