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

By U. Javed, M. M. Riaz, A. Ghafoor, and T. A. Cheema

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

Citation:
U. Javed, M. M. Riaz, A. Ghafoor, and T. A. Cheema, "MRI Brain Classification Using Texture Features, Fuzzy Weighting and Support Vector Machine," Progress In Electromagnetics Research B, Vol. 53, 73-88, 2013.
doi:10.2528/PIERB13052805

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