Categorization of HE Stained Breast Tissue Samples at Low Magnification by Nuclear Aggregations

Machine Learning Methods for Histopathological Image Analysis: A Review

“…The features were obtained based on the area (object and convex hull area), and perimeter of threshold highlighted objects. Marugame et al [48] used morphometric features extracted from image objects indicating nuclear aggregations to represent three categories of ductal carcinomas in breast HIs. The number of pixels, length, and thickness of the objects reflect their size and shape.…”

“…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,…”

“…Several works have employed such classifiers with different feature categories. Marugame et al [48] proposed a simple classifier based on Bayes theory to classify ductal carcinomas into three categories. Specialists consulted by authors claimed that the simple classifier provides, together with the morphological features, a better way to understand the results.…”

“…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

“…The features were obtained based on the area (object and convex hull area) and perimeter of threshold highlighted objects. Marugame et al [49] used morphometric features extracted from image objects indicating nuclear aggregations to represent three categories of ductal carcinomas in breast HIs. The number of pixels, length, and thickness of the objects reflect their size and shape.…”