Segmentation and Classification of Brain CT Images Using Combined Wavelet Statistical Texture Features

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a method to find and select both the dominant run length and co-occurrence texture features of the wavelet approximation tumor region of each slice to be segmented by support vector machine. Two dimensional discrete wavelet decomposition is performed on the tumor image to remove the noise. The images considered for this study belong to 192 benign and malignant tumor slices. A total of 17 features are extracted and six features are selected using Student's t test. The reduced optimal features are used to model and train the probabilistic neural network classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features that have important contribution in classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed system is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.