Image segmentation using nearest neighbor classifiers based on kernel formation for medical images

Image Segmentation is one of the significant elements in the part of image processing. It becomes most essential demanding factor while typically dealing with medical image segmentation. In this paper, proposal of our work comprises of formation of kernel for the medical images by performing the deviation of mapped image data within the scope of each region from the piecewise constant model and based on the regularization term based on the function of indices value of the region. The functional objective minimization is carried out by two steps minimization in image segmentation using graph cut methods, and minimization with respect to region parameters using constant point computation. Nearest neighbor classifiers are introduced to the benchmarked image data segmented portions. Among the different methods in supervised statistical pattern recognition, the nearest neighbor rule results in achieving high performance without requirement of the prior assumptions about the distributions from which the training sets are taken.