A COMPARATIVE STUDY ON CLASSIFICATION OF IMAGE SEGMENTATION METHODS WITH A FOCUS ON GRAPH BASED TECHNIQUES

Image segmentation is the most precarious functions in image processing and analysis. Basically segmentation consequences influence all the subsequent processes of image analysis such as object description and illustration, characteristic dimension, and even the subsequent higher level tasks such as classification of object. Hence, image segmentation is the most important and critical process for assisting the, depiction, delineation and visualization of regions of interest in any image. Physical segmentation of an image is not only a tiresome and time consuming process, but also not exceptionally accurate particularly with the increasing imaging modalities and uncontrollable quantity of images that need to be observed. Therefore it becomes essential to examination current methodologies of image segmentation using computerized algorithms that are precise and entail as little user interaction as probable in particular for medical images. In the image segmentation process, the anatomical organization or the region of interest needs to be defined and extracted out so that it can be viewed independently. In this comparative paper we venture the significant place of segmentation of images in pulling out information for decision making. Afterwards we present the most relative and general methods which we have classified into three categories, first category represents pixel based, second edge based and third represents region based methods in particular graph based methods, highlighting the weaknesses and strengths according to appropriateness for image segmentation applications.

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