Quantitative Analysis Of Segmentation Methods On Ultrasound Kidney Image

Abstract: Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. The most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process and inherently limits the depth of evaluation to a relatively small number of segmentation comparisons over a predetermined set of images. The purpose of this paper is to describe a framework for evaluating ultrasound kidney image segmentation using various algorithms like Edge detection; Watershed segmentation; Region based segmentation and Clustering Method. We prove here that the K-means clustering gives better result for kidney image segmentation because of less intensity variations in ultrasound kidney image and the results are compared with other segmentation methods.

Keywords: Image Segmentation, Objective evaluation, Edge Detection, Watershed, Region Based, Clustering Method.