Image segmentation of an adaptive threshold algorithm using watershed transform and fuzzy c-means clustering on level set method

A new method for image segmentation is proposed in this paper, which combines the adaptive threshold algorithm, watershed transform, FCM and level set method. When using thresholding method to segment an image, a fixed threshold is not suitable if the background is rough here, we propose a new adaptive thresholding method using level set theory. The method requires only one parameter to be selected and the adaptive threshold surface can be found automatically from the original image. The watershed transform is first used to presegment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The fuzzy c-means (FCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. FCM algorithm computes the fuzzy membership values for each pixel. On the basis of FCM the edge indicator function was redefined. Using the edge indicator function of a image was performed to extract the boundaries of objects on the basis of the presegmentation. Therefore, the proposed method is computationally efficient. Moreover, the algorithm can localize the boundary of the regions exactly due to the edges obtained by the watersheds. The efficiency and accuracy of the algorithm is demonstrated by the experiments on the images. The above process of segmentation showed a considerable improvement in the evolution of the level set function.