Texture feature coding method for classification of liver sonography

Usefulness of textural analysis as a tool for noninvasive liver fibrosis staging

“…Gaitini et al [26] included 44 patients, Li et al [5] included 6 patients, Cao et al [8,25] included 36 patients, Horng [27] included 40 patients, Yeh et al [35] included 20 fresh human liver samples obtained from surgical specimens, Badawi et al [13] included 140 patients, Mojsilovic et al [11] included 30 patients, Kadah et al [34] included 120 patients, Abe et al [6] included 22 patients, and Wu et al [7] included 45 patients. Other authors did not report the number of patients, only the number of images [10,12,14,24,36]. Our textural analysis system included almost all of the textural algorithms encountered in the fibrosis detection literature.…”

“…The resolution cell is assumed to be 0.28 9 0.28 mm 2 . Computation times are evaluated for a single core algorithms and the papers that use these algorithms in fibrosis detection (when possible): first order statistics [13,34], gray tone difference matrix [33], gray level cooccurrence matrix [7,13,34,35], multiresolution fractal dimension [7], differential box counting [8,36], morphological fractal dimension estimators [37], Fourier power spectrum [6,7], Gabor filters [12], Law's energy measures [7], texture edge co-occurrence matrix [8], phase congruency [24], and texture feature coding matrix [14]. Twelve algorithms that process the entire ROI were implemented and 234 features were computed per image.…”

“…There are several approaches found in the literature. Some authors have evaluated the RF signal [1,2], while others have proposed some visual scores [3,4], but most authors have employed a texture analysis system to discriminate between various fibrosis stages [5][6][7][8][9][10][11][12][13][14].…”

Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

“…There are two kinds to construct a multi-class classifier. One is to directly develop multiclass algorithms such as the maximum likelihood classifier (Horng, Sun, & Lin, 2002), radial basis function neural network (Horng, 2007;Subashini, Ramalingam, & Palanivel, 2009), and the other decomposes a multi-class problem to several two-class problems such as multi-class support vector machines (Horng, 2009). The main objective of the proposed work is to explore the classification of the various classifiers that are maximum likelihood classifier, radial basis function neural network and multi-class support vector machine classifiers.…”

Intelligence control of on-line dynamic gray cloth inspecting machine system module design. II. Defects inspecting module design

“…As co-occurrence matrix was representative feature expression method in texture analysis [7][8][9][10][11]. This approach established co-occurrence matrix of spatial gray level from probability density function of gray level change of pixels in original image at specific neighboring location.…”