CLASSIFYING OF MRI IMAGES FOR CEREBRAL TUMOR USING SOFT COMPUTING TECHNIQUES

Healthcare scientists determined how MRI images have indeed been highly beneficial in latest times in the investigation of the recognition and early identification of a brain disease. The main primary stages in analyzing the brain MRI pictures are image pre-processing, segmentation, feature extraction, and classification. Among the crucial processes that can evaluate how well brain MRI scans can be classified and ultimately the condition it will indicate is feature extraction and segmentation. In this paper stage wise methods are described. In the first stage (pre-processing stage) different filters; like; median, weiner, anistropic, non local means as well as combined filters used. In the pre-processing part, combined weiner and anistropic filter gives the best result. In the second stage (segmentation stage), multi-thresholding technique – cuckoo search algorithm used using different objective functions; like; ostu, kapur entropy, tsallis entropy and proposed. In the proposed method of the segmentation stage used cuckoo search algorithm using combined ostu and tsallis entopy as an objective function. In the third stage (feature extraction), discrete wavelet transform used and in the fourth stage (classification) support vector machine used. In each stage results are compared using different parameters and we got best output using proposed method.