Wavelet based automatic segmentation of brain tumors using optimal texture features

Abstract

This paper presents an automated segmentation of brain tumors in magnetic resonance images (MRI) using optimal texture features. In this work, a wavelet based texture feature set is derived. In the proposed method the optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Artificial neural network classifier is employed to evaluate the performance of textural features. A very difficult problem in classification techniques is the choice of features to distinguish between classes. The feature optimization problem is addressed by using a genetic algorithm (GA) as a search method. These 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. © 2008 Springer-Verlag.