Texture analysis techniques for the classification of microcalcifications in digitised mammograms

Computerized Image Analysis of Mammographic Microcalcifications: Diagnosis and Prognosis

“…As compared to other texture-based CADx schemes analyzing ROIs containing the cluster (Dhawan et al, 1996;Kocur et al, 1996;Chan et al, 1997;Chan et al, 1998;Kramer & Aghdasi 1999;Soltanian-Zadeh et al, 2004), the performance achieved by the wavelet texture signatures, employing the MCs surrounding tissue approach, is also comparable. However, heterogeneity of the datasets analyzed renders direct comparison not feasible.…”

“…Features extracted from GLCMs provide information concerning image texture heterogeneity and coarseness, which is not necessarily visually perceived. The discriminating ability of GLCMs features, as extracted from original image ROIs containing MCs, has been demonstrated by most studies (Dhawan et al, 1996;Kocur et al, 1996;Kramer & Aghdasi 1999;Chan et al, 1997;Chan et al, 1998;Soltanian-Zadeh et al, 2004), with specific GLCMs feature combinations achieving an (Chan et al, 1997). In addition, GLCMs feature have shown to be more effective than morphology-based features (Chan et al, 1998), while their combination can provide an even higher classification performance.…”

“…An obvious extension of wavelet FOS is the computation of co-occurrence matrix features from wavelet decomposed subimages, to describe coefficients second order statistics (Van de Wouwer et al, 1998). Kramer and Aghdasi (1999) demonstrated that co-occurrence matrices features extracted from wavelet decomposed subimages were superior to GLCMs and wavelet FOS in discriminating malignant from benign MC clusters.…”

A New CAD System for Breast Microcalcifications Diagnosis

“…Several classifiers were used to distinguish malignant lesions from benign ones [14,60], but the most commonly used are Neural networks [23,35,39], K nearest neighbors [44], Bayesian classifier [39,57,61], Quadratic classifier [52], Linear classifier [20], Expert system [18], Binary decision tree [71], Genetic algorithms [11], SVM [25,58] and Adaptive thresholding [67].…”

Methods for iris classification and macro feature detection

“…The first order gray level histogram represents the probability of occurrence of gray levels in an image [41]. A gray level co-occurrence matrix is considered to measure the second order histogram as it considers gray level distributions of pairs of pixels in each direction [41].…”