Improved Fuzzy C-Means Clustering Algorithm Using Watershed Transform on Level Set Method for Image Segmentation
Tara Saikumar, P. Yugander, P. S. Murthy, Smitha B A · International Journal of Machine Learning and Computing · 2012
An innovative novel method for image segmentation is proposed in this paper, which combines the watershed transform, Improved FCM and level set method. The watershed transform is original used to pre-segment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The Improved fuzzy c-means (IFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. IFCM algorithm computes the fuzzy membership values for each pixel. On the source of IFCM the edge indicator function was redefined. Using the edge indicator function of an image was performed to extract the boundaries of objects on the origin of the pre-segmentation. Therefore, the proposed method is computationally efficient. The efficiency and accuracy of the algorithm is demonstrated. The above process of segmentation showed a considerable improvement in the evolution of the level set function.