Particle Swarm Optimization Methods for Image Segmentation Applied In Mammography

Accurate medical diagnosis requires a segmentation of large number of medical images. The automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. Image segmentation refers to the process that partitions an image into mutually exclusive regions that cover the image. Among the various image segmentation techniques, traditional image segmentation methods are widely used but have certain drawbacks, which cannot be used for accurate result. In this thesis clustering based techniques is employed on images which results into segmentation of images. The performance of Fuzzy C-means (FCM) integrated with Particle Swarm optimization (PSO) technique and its variations are analyzed in different application fields. To analyze techniques in different fields several metrics are used namely global consistency error, probabilistic rand index and variation of information are used. 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. Finally, fractional order Darwinian PSO along with neighbourhood Fuzzy C-means and partial differential equation based level set method is an effective image segmentation technique to study the intricate contours. Keywords— Darwinian PSO (DPSO), Fuzzy C-means (FCM), FCM neighbourhood (FCMN), Fractional Order DPSO (FO-DPSO), Particle Swarm Optimization (PSO),