An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm

Image segmentation is a significant task for image analysis which is at the middle layer of image engineering. The purpose of segmentation is to decompose the image into parts that are meaningful with respect to a particular application, very much like the idea of separating figure from ground. With the advance in color image technology and its application in various areas, several techniques have been formulated. The proposed system is to boost the morphological watershed method for degraded images. The watersheds transformation is an effective method for extracting out continuous boundaries of each region and gives solid results. However, some of the issues concerned with it are, over segmentation and ambiguous boundary on homogeneous regions. Proposed algorithm is based on merging enhanced morphological watershed result with enhanced edge detection result obtain on pre processing of degraded images. Here preprocessing means image restoration. Hence it enhances the watershed result. As a post processing step, to each of the segmented regions obtained, color histogram algorithm is applied i.e computing the JND histogram followed by the agglomeration of the histograms which can be thought of as the powerful alternatives to the other image thresholding techniques. This stimulates the process of merging of small left over segments with larger similar segments, enhancing the overall performance of the watershed algorithm. The Segmentation algorithm is more computationally efficient, its edge- preserving, noise removing, shape maintaining features are remarkable than many top-ranking image segmentation algorithms.