A Novel Based Approach for Extraction of Brain Tumor in MRI Images Using Soft Computing Techniques

Abstract: Brain tumor diagnosis is a very crucial task. Magnetic resonance imaging (MRI) scan can be used to produce image of any part of the body and it provides an efficient and fast way for diagnosis of the brain tumor. In the Existing Method, K-nearest neighbor is used to classify subject as normal or abnormal image. In the Proposed method an efficient detection of brain tumor region from cerebral image is done using Fuzzy C-means clustering and histogram .The histogram equalization calculates the intensity values of the grey level images and decomposition of image are extracted using principle component analysis is used to reduce dimensionality of the wavelet co-efficient. The Fuzzy C-means clustering algorithm finds the centroids of the cluster groups together the Brain tumor patterns obtained from MRI images. Segmentation result shows the extract suspicious tumor region.

Keywords: Magnetic resonance imaging (MRI), K-nearest neighbor, Fuzzy C-means clustering, Histogram equalization, Segmentation.