J Med Syst - Statistical analysis of textural features for improved classification of oral histopathological images.

Tópicos

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Resumo

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

Resumo Limpo

object paper provid improv techniqu can assist oncopathologist correct screen oral precancer condit special oral submuc fibrosi osf signific accuraci basi collagen fibr subepitheli connect tissu propos scheme compos collagen fibr segment textur featur extract select screen perfom enhanc gaussian transform final classif studi collagen fibr segment rgb color channel use backprobag neural network normal osf histolog imag follow histogram specif reduc stain intens variat henceforth textur featur collgen area extract use fractal approach viz differenti box count brownian motion curv featur select done use kullbackleibl kl diverg criterion screen perform evalu base various statist test conform gaussian natur screen perform enhanc gaussian transform nongaussian featur use hybrid distribut moreov routin screen design base two statist classifi viz bayesian classif support vector machin svm classifi normal osf observ svm linear kernel function provid better classif accuraci compar bayesian classifi addit fractal featur collagen gaussian transform improv bayesian classifi perform result studi discuss

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