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The research work presented in this paper is to achieve the tissueclassification and automatically diagnosis the abnormal tumor region present inComputed Tomography (CT) images using the wavelet based statistical textureanalysis method. Comparative studies of texture analysis method are performedfor the proposed wavelet based texture analysis method and Spatial Gray LevelDependence Method (SGLDM). Our proposed system consists of four phases i)Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection(iv) Analysis of extracted texture features by classifier. A wavelet basedstatistical texture feature set is derived from normal and tumor regions.Genetic Algorithm (GA) is used to select the optimal texture features from theset of extracted texture features. We construct the Support Vector Machine(SVM) based classifier and evaluate the performance of classifier by comparingthe classification results of the SVM based classifier with the BackPropagation Neural network classifier(BPN). The results of Support VectorMachine (SVM), BPN classifiers for the texture analysis methods are evaluatedusing Receiver Operating Characteristic (ROC) analysis. Experimental resultsshow that the classification accuracy of SVM is 96% for 10 fold crossvalidation method. The system has been tested with a number of real ComputedTomography brain images and has achieved satisfactory results.

Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography Images Using Wavelet Based Statistical Texture Features