In this research work clustering based techniques is employed on images which results into segmentation of images. The performance of Fuzzy C-means (FCM) integrated with the Particle Swarm optimization (PSO) technique and its variations are analyzed in different application fields. To analyze and grade the performance, computational and time complexity of techniques in different fields several metrics are used. Then the four well known techniques of image segmentation namely Fuzzy C-means algorithm, neighborhood information, Partial differential equation based level set methods and Particle swarm optimization based fractional order Darwinian particle swarm optimization are integrated in order to obtain the improved segmentation. This experimental performance analysis shows that FCM along with fractional order Darwinian PSO give better performance in terms of classification accuracy, as compared to other variation of other techniques used. The integrated algorithm tested on images proves to give better results visually as well as objectively and are more real time compatible.