  The research work presented in this paper is to achieve the tissue
classification and automatically diagnosis the abnormal tumor region present in
Computed Tomography (CT) images using the wavelet based statistical texture
analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level
Dependence 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 based
statistical texture feature set is derived from normal and tumor regions.
Genetic Algorithm (GA) is used to select the optimal texture features from the
set of extracted texture features. We construct the Support Vector Machine
(SVM) based classifier and evaluate the performance of classifier by comparing
the classification results of the SVM based classifier with the Back
Propagation Neural network classifier(BPN). The results of Support Vector
Machine (SVM), BPN classifiers for the texture analysis methods are evaluated
using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross
validation method. The system has been tested with a number of real Computed
Tomography brain images and has achieved satisfactory results.
