A novel approach for medical imagery storage by classifying the dissimilar regions

Everyday medical is capturing thousands of images which need to be classified in a proper way. In this paper, we address the problem of replacing the existing images with the captured one. We provide a new solution by storing only the nonexisting part of the image. Though medical images have been classified in past by using various techniques, the researchers are always finding alternative strategies for medical image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. In order to overcome this difficulty, we propose an efficient approach, which consists of an algorithm that can adopt robust feature kernel principle component analysis (KPCA) to reduce dimensionality of image. Concerning image clustering, we utilize Fuzzy N-Means algorithm. Finally data is stored into database according to specific class by utilizing support vector machine classifier. Thus the proposed scheme improve the efficient storage of medical images in the database, save time consumption and make the correction of the medical images more proficiently.