Automatic Tumor Segmentation using Optimal Texture Features

This paper presents an automatic segmentation of malignant tumor in magnetic resonance images (MRI´s) of brain using optimal texture features. Texture features are extracted from normal and tumor regions (ROI) in the brain images under study using spatial gray level dependence method and wavelet transform. The normal and tumor regions are classified using an artificial neural network. A very difficult problem in classification techniques is the choice of features to distinguish between classes. In the proposed method, the optimal texture features that distinguish between the brain tissue and malignant tumor tissue is found using genetic algorithm (GA). The optimal features are used to segment the tumor. The proposed feature based segmentation technique is compared with few existing techniques. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.