DETECTION OF AFFCTED CLUSTR USING INTERCTION K-MEANS FOR BRAIN TUMOR
Brain tumor extraction and its analysis are challenging tasks in medical image processing because brain image and its structure is complicated that can be analyzed only by expert radiologists. Segmentation plays an important role in the processing of medical images. Magnetic Resonance Imaging has become a particularly useful medical diagnostic tool for diagnosis of brain and other medical images. Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or more formally, has discontinuities. Edge detection is a fundamental tool in image processing . To make more of the potentially available information accessible, they need effective and efficient multivariate data mining methods. A cluster is a collection of similar objects and so clustering technique is used to detect and make a group of similar patterns with the help of clustering techniques and capture the different interaction patterns in healthy and diseased subjects. Interaction K-Means clustering is used which differentiates the normal and diseased clusters. In the proposed system, Interaction K-Means clustering with Ranking algorithm is together used which improves the efficiency by finding the best affected cluster among several clusters and lists the clusters from diseased to normal.