Automatic classification of brain computed tomogra

Authors: A Padma Nanthagopal R Sukanesh Rajamony

Publish Date: 2012/08/05

Volume: 15, Issue: 4, Pages: 363-372

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Abstract

Automated and accurate classification of computed tomography CT images is an integral component of the analysis and interpretation of neuro imaging In this paper we present the waveletbased statistical texture analysis method for the classification of brain tissues into normal benign malignant tumor of CT images Comparative studies of texture analysis method are performed for the proposed texture analysis method and spatial gray level dependence matrix method SGLDM Our proposed system consists of five phases i image acquisition ii discrete wavelet decomposition DWT iii feature extraction iv feature selection and v analysis of extracted texture features by classifier A waveletbased statistical texture feature set is derived from two level discrete wavelet transformed approximation low frequency part of the image sub image Genetic algorithm GA and principal component analysis PCA are used to select the optimal texture features from the set of extracted features The support vector machine SVM is employed as a classifier The results of SVM for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic ROC analysis The experimental results show that the proposed system is able to achieve higher classification accuracy effectiveness as measured by sensitivity and specificity

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