Implementation and quality measures of graph theory model based image segmentation process in medical application

Abstract

Abstract This research work simplified the representation of an image into more significant and easier way to analyse the image segmentation process by applying graph theory using color spatial clustering with consensus region merging. The color spatial clustering with consensus region merging is compared with other traditional and graph theory model to analyse the various quality measures calculated for the input of magnetic resonance imaging (MRI) scan and X-Ray images which will be useful in medical imaging for better analysis during diagnosis. From quality measures, the proposed method shows good quality image parameters as it has lower MSE, NAE values.