JSE_V3_N3_RP4 C. Naga Raju L.S.S. Reddy Journal on Software Engineering 2230 – 7168 3 3 52 56 Machine-learned Pixel Classification, Regional Minimum, Bayesian Perspective, Markov Random Fields, Watershed Image segmentation is one of the important areas of current research. This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. The authors have used the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Extracting internal markers from the aforementioned region probability map by using higher thresholds still results in a poor object. This method works for low contrast edge detection of images. This could not produce better result for Blurred images to image Analyze and classify the images. By applying this method one can enhance the edge. The authors of the paper have taken this concept from references cited in the paper and implemented it and produced results in the paper. After that they have modified the method by applying thinning technique based on erosion and got good results than existing method. And they found that, it is good for medical images. January - March 2009 Copyright © 2009 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=193