Detection and Extraction of Abnormality from Brain MRI Image Using Extended Fuzzy-C-Means Clustering Algorithm

Computerized automated detection of brain disorders is complex as it heavily depends upon the imaging technique, shape and size of the brain and resolution of the image. In this paper we are going to give an efficient algorithm to detect and extract abnormality from brain MRI using Extended-FCM and Density-based clustering technique which perfectly separates out the abnormal lesion with lesser number of input parameters required. Our all new extended-FCM proves to be efficient here, as in 92% of the cases it gives clustered output having high Silhouette index in lesser number of iterations. This approach will facilitate automated detection of brain diseases in rural and remote areas.