<title>Edge detection and image segmentation based on K-means and watershed techniques</title>

Abstract

In this paper, we present a method that incorporates k-means and watershed segmentation techniques for performing image segmentation and edge detection tasks. Firstly we used k-means techniques to examine each pixel in the image and assigns it to one of the clusters depending on the minimum distance to obtain primary segmented image into different intensity regions. We then employ a watershed transformation technique works on that image. This includes: First, Gradient of the segmented image. Second, Divide the image into markers. Third, Check the Marker Image to see if it has zero points (watershed lines) then delete the watershed lines in the Marker Image created by watershed algorithm. Fourth, Create Region Adjacency Graph (RAG) and the Region Adjacency Boundary (RAB) between two regions from Marker Image and finally; Fifth, Region Merging according to region average intensity and edge strength (T1, T2), where all the regions with the same merged label belong to one region. Our approach was tested on remote sensing and brain MR medical images and the final segmentation is one closed boundary per actual region in the image.