A feature selection based framework for histology image classification using global and local heterogeneity quantification

Abstract

Biopsy remains the gold standard for the diagnosis of chronic liver diseases. However, the concordance between readers is subject to variability causing an increasing need of objective tissue description methods. A complete framework has been implemented to analyze histological images from any kind of tissue. Based on the feature selection approach, it computes the most relevant subset of descriptors in terms of classification from a wide initial list of local and global descriptors. In comparison with equivalent methods, this implementation is able to find lists of descriptors which are significantly shorter for an equivalent accuracy and furthermore it enables the classification of slides using combinations of global and local measurements. The results have pointed that it could reach an accuracy of 82.8% in a human liver fibrosis grading approach by selecting 6 descriptors from an initial set of 258 global and local descriptors.

Machine Learning Methods for Histopathological Image Analysis: A Review

“…They used cell-level morphometric features such as central power sums, area, radius, perimeter, and roundness of segments, maximum, mean, and minimum intensity, and intensity covariance, variance, skewness, and kurtosis within regions and patch-level features such as LBP, SIFT, and color histograms from segmented images using the wa-tershed algorithm. Coatelen et al [82,83] proposed a feature selection method of liver HI classification based on morphometric features such as area, compactness, perimeter, aspect ratio, Zernick moment, etc., textural features such as GLCM, LBP, fractal dimension, Fourier distance, etc., and structural or graph-based features such as the number of nodes/edges, modularity, pi, eta, theta, beta, alpha, gamma, and Shimbel indexes, etc. Two greedy algorithms (fselector and in-house recursive) selected features in a pool of 200 features where an SVM classifier implemented the fitness function.…”

“…Year Tissue/ Feature Organ Caicedo et al [72] 2008 Skin Color and gray histograms, LBP, Tamura Ballarò et al [39] 2008 Bone Morphometric Marugame et al [48] 2009 Breast Morphometric Kong et al [86] 2009 Brain Textural, morphological Kuse et al [52] 2010 Lymph nodes GLCM Orlov et al [78] 2010 Lymph nodes Zernike, Chebychev, Chebyshev-Fourier, color histograms, GLCM, Tamura, Gabor, Haralick, edge statistics Petushi et al [40] 2011 Breast Morphometric Madabhushi et al [41] 2011 Prostate Voronoi diagram, Delaunay triangulation, minimum spanning tree, nuclear statistics Osborne et al [49] 2011 Skin Morphometric Caicedo et al [53] 2011 Skin Gray, color, invariant feature, Sobel, Tamura LBP, SIFT Huang et al [62] 2011 Breast Receptive field, sparse coding Cruz-Roa et al [77] 2011 Skin SIFT, luminance, DCT Loeffler et al [47] 2012 Prostate Morphometric Song et al [42] 2013 Pancreas Morphometric Gorelick et al [43] 2013 Prostate Morphometric, geometric Filipczuk et al [44] 2013 Breast Morphometric Atupelage et al [61] 2013 Blood Fractal dimension Basavanhally et al [75] 2013 Breast Morphological, textural, graph-based De et al [79] 2013 Uterus GLCM, Delaunay triangulation, weighted density distribution Ozolek et al [45] 2014 Thyroid Linear optimal transport Olgun et al [51] 2014 Colorectal Local object pattern Michail et al [84] 2014 Lymph nodes Morphometric, texture Vanderbeck et al [80] 2014 Liver Morphological, textural, pixel neighboring statistics Kandemir et al [81] 2014 Esophagus Morphometric, LBP, SIFT, color histograms Fernández-Carrobles et al [54] 2015 Breast Textons Gertych et al [59] 2015 Prostate LBP Tashk et al [76] 2015 Breast LBP, morphometric, statistical Coatelen et al [82,83] 2015 Liver Morphometric, GLCM, LBP, fractal dimension,…”

“…Year Tissue/Organ Classifier Caicedo et al [72] 2008 Skin SVM Ballarò et al [39] 2008 Bone DT, k-NN Mete and Topaloglu [94] 2009 Skin DT, NB, SVM Marugame et al [48] 2009 Breast Bayes Orlov et al [78] 2010 Lymph nodes k-NN, NB, RBF Osborne et al [49] 2011 Skin SVM Malon et al [87] 2012 Breast SVM Sidiropoulos et al [95] 2012 Brain PNN De et al [79] 2013 Uterus LDA Atupelage et al [61] 2013 Liver SVM Cosatto et al [102] 2013 Gastric MLP (MIL) Homeyer et al [106] 2013 Liver k-NN, NB, RF Song et al [42] 2013 Pancreas k-NN, NB, NN, SVM Irshad et al [105] 2014 Breast DT, MLP, SVM Xu et al [103] 2014 Colorectal Gaussian (MIL) Kandemir et al [81] 2014 Gastric SVM (MIL) Olgun et al [51] 2014 Colon SVM Vanderbeck et al [80] 2014 Liver SVM Coatelen et al [82,83] 2014 Liver SVM Michail et al [96] 2014 Lymph nodes LDA…”

Machine Learning Methods for Histopathological Image Analysis: A Review

“…They used cell-level morphometric features such as central power sums, area, radius, perimeter, and roundness of segments, maximum, mean, and minimum intensity, and intensity covariance, variance, skewness, and kurtosis within regions and patch-level features such as LBP, SIFT and color histograms from segmented images using the watershed algorithm. Coatelen et al [83][84] proposed a feature selection method of liver HI classification based on morphometric features such as area, compactness, perimeter, aspect ratio, Zernick moment, etc., textural features such as GLCM, LBP, fractal dimension, Fourier distance, etc., and structural or graph-based features such as number of nodes/edges, modularity, pi, eta, theta, beta, alpha, gamma and Shimbel indexes, etc. Two greedy algorithms (fselector and in-house recursive) selected features in a pool of 200 features where the fitness function was implemented by an SVM classifier.…”

“…Year Tissue / Feature Organ Caicedo et al [73] 2008 Skin Color and gray histograms, LBP, Tamura Ballarò et al [40] 2008 Bone Morphometric Marugame et al [49] 2009 Breast Morphometric Kong et al [87] 2009 Brain Textural, morphological Kuse et al [53] 2010 Lymph nodes GLCM Orlov et al [79] 2010 Lymph nodes Zernike, Chebychev, Chebyshev-Fourier, color histograms, GLCM, Tamura, Gabor, Haralick, edge statistics Petushi et al [41] 2011 Breast Morphometric Madabhushi et al [42] 2011 Prostate Voronoi diagram, Delaunay triangulation, minimum spanning tree, nuclear statistics Osborne et al [50] 2011 Skin Morphometric Caicedo et al [54] 2011 Skin Gray, color, invariant feature, Sobel, Tamura LBP, SIFT Huang et al [63] 2011 Breast Receptive field, sparse coding Cruz-Roa et al [78] 2011 Skin SIFT, luminance, DCT Loeffler et al [48] 2012 Prostate Morphometric Song et al [43] 2013 Pancreas Morphometric Gorelick et al [44] 2013 Prostate Morphometric, geometric Filipczuk et al [45] 2013 Breast Morphometric Atupelage et al [62] 2013 Blood Fractal dimension Basavanhally et al [76] 2013 Breast Morphological, textural, graph-based De et al [80] 2013 Uterus GLCM, Delaunay triangulation, weighted density distribution Ozolek et al [46] 2014 Thyroid Linear optimal transport Olgun et al [52] 2014 Colorectal Local object pattern Michail et al [85] 2014 Lymph nodes Morphometric, texture Vanderbeck et al [81] 2014 Liver Morphological, textural, pixel neighboring statistics Kandemir et al [82] 2014 Esophagus Morphometric, LBP, SIFT, color histograms Fernández-Carrobles et al [55] 2015 Breast Textons Gertych et al [60] 2015 Prostate LBP Tashk et al [77] 2015 Breast LBP, morphometric, statistical Coatelen et al [83] [84] 2015 Liver Morphometric, GLCM, LBP, fractal dimension, Graph-based Balazsi et al [61] 2016 Breast LBP Fukuma et al …”