Performance analysis of image segmentation using watershed algorithm, fuzzy C-means of clustering algorithm and Simulink design

An Integration of image processing and soft computing techniques for the image analysis plays a significant contribution in the field of image processing. One of the important class of image analysis techniques is image segmentation. There are applications in various fields some of them are sorting product in the industry, surveillance system in the security zones, biomedical processing, medical imaging, environmental predictions etc. In this paper the study of fuzzy C-means clustering method, watershed based image segmentation and Simulink design based image segmentation with some enhancement design algorithm for the improvement of the overall performance of image segmentation is used. The main objective of this paper is to bring the conviction of utility of watershed, Simulink and FCM based segmentation for the applications in medical imaging, and other images where lots of spatial & inherent information is available, by considering their performance analysis metrics. This performance analysis is used to justify and decide which method is appropriate and more convincing for the effective analysis of more complex imaging system.