MRI BRAIN CLASSIFICATION USING TEXTURE FEATURES, FUZZY WEIGHTING AND SUPPORT VECTOR MACHINE

A technique for magnetic resonance brain image classifl- cation using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifles the magnetic resonance brain images into normal and abnormal classes, classiflcation 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 classiflcation accuracy. Multi- class support vector machine is used for classiflcation purpose. Results demonstrate that the classiflcation accuracy of the proposed scheme is better than the state of art existing techniques.

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