Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier

Due to the acquisition of huge amount of brain tumor magnetic resonance images (MRI) in the clinics, it is very difficult for the radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems increase the diagnostic abilities of radiologists and reduce the elapsed time for perfect diagnosis. An intelligent computer-aided technique is proposed in this paper for automatic detection of brain tumor from MR images. The proposed technique uses following computational methods; the K-means clustering for segmentation of brain tumor from other brain parts, extraction of features from this segmented brain tumor portion using gray level co-occurrence Matrices (GLCM), and the support vector machine (SVM) to classify input MRI images into normal and abnormal. The whole work is carried out on 64 images consisting of 22 normal and 42 images having brain tumor (benign and malignant). The overall classification accuracy using this method is found to be 99.28% which is significantly good.

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