Classification of benign and malignant brain tumor CT images using wavelet texture parameters and neural network classifier

Computational methods are useful for medical diagnosis because they provide additional information that cannot be obtained by simple visual interpretation. As a result, an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. In this paper, we present the combination of wavelet statistical texture features (WST) obtained from two-level discrete wavelet-transformed (DWT) images and wavelet co-occurrence texture features (WCT) obtained from two-level DWT detail images for the classification of abnormal brain tissues into benign, malignant tumor of CT images. Our proposed system consists of four phases: (1) segmentation of region of interest, (2) discrete wavelet decomposition, (3) feature extraction and feature selection, and (4) classification and evaluation. The support vector machine is employed to segment the shape of tumor information. A combination of both WST and WCT texture features is extracted from tumor region of two-level discrete wavelet-transformed images. Genetic algorithm (GA) is used to select the optimal texture features from the set of extracted features. The probabilistic neural network classifier (PNN) is built to classify the abnormal brain tissues into benign, malignant tumor images and evaluate the performance of classifier by comparing the classification results of the PNN classifier with linear vector quantization (LVQ) neural network classifier, back propagation neural network (BPN) classifier. The results of PNN, LVQ, BPN classifiers for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic analysis. From the experimental results, it is inferred that the best classification performance is achieved by PNN than LVQ and BPN classifiers. The system has been tested with real data of 80 benign, malignant CT brain tumor images and has achieved satisfactory results.