Segmentation of medical images using Simulated Annealing Based Fuzzy C Means algorithm

Abstract

Accurate segmentation is desirable for analysis and diagnosis of medical images. This study provides methodology for fully automated simulated annealing based fuzzy c-means algorithm, modelled as graph search method. The approach is unsupervised based on pixel clustering using textural features. The virtually training free algorithm needs initial temperature and cooling rate as input parameters. Experimentation on more than 180 MR and CT images for different parameter values, has suggested the best-suited values for accurate segmentation. An overall 97% correct segmentation has been achieved. The results, evaluated by radiologists, are of clinical importance for segmentation and classification of Region of Interest.