MRI brain tumor images classification using K-means clustering, NSCT and SVM

Magnetic Resonance Imaging (MRI) has justified its potential to be used as an emerging imaging tool for the anatomical examination of tissue in brain. Manual classification of a large number of MRI brain images of patients is anextremely time killing process and also susceptible to errors, so there is a need to develop a fast, accurate and automatic scheme for classifying MRI brain images. In this paper we present a scheme for the automatic classification of MRI brain images as normal, where tumor is not present or abnormal, where tumor is present using K-Means clustering, nonsubsampledcontourlet transform (NSCT) and support vector machine (SVM). In the preprocessing stage of this proposed scheme, median filter is used for removing noise and enhancing resolution of MRI brain images. Then K-means clustering is used for segmenting MRI brain imagesbecause it segments an image faster. Because of salient properties of NSCT like multiscale, multidirection and shift invariance, NSCT is applied to the segmented image. Then seven features are extracted from subband coefficients of NSCTand these features are applied to support vector machine for the classification of MRI brain images. The efficacy of this scheme is evaluated in terms of sensitivity, specificity and accuracy and this scheme exhibits classification accuracy rate 98.86% for GRB kernel SVM. This scheme is compared with other MRI brain images classification schemes and provides satisfactory results which clarify the effectiveness of our scheme.