MRI brain classification using texture features, fuzzy weighting and support vector machine

Abstract

A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifies the magnetic resonance brain images into normal and abnormal classes, classification with in the abnormal brain which is relatively hard and challenging problem is addressed here. Texture features along with invariant moments are extracted and the weights are assigned to each feature to increase classification accuracy. Multiclass support vector machine is used for classification purpose. Results demonstrate that the classification accuracy of the proposed scheme is better than the state of art existing techniques.