IMAGE SEGMENTATION AVAILABLE TECHNIQUES, OPEN ISSUES AND REGION GROWING ALGORITHM

In areas such as computer vision and mage processing, image segmentation has been and still is a relevant research area due to its wide spread usage and application. This paper provides a survey of achievements, problems being encountered, and the open issues in the research area of image segmentation and usage of the techniques in different areas.. We considered the techniques under the following three groups: Threshold-based, Edge-based and Region-based. Region Growing is an approach to image segmentation in which neighboring pixels are examined and added to a region class if no edges are detected. This process is iterated for each boundary pixel in the region. If adjacent regions are found, a region-merging algorithm is used in which weak edges are dissolved and strong edges are leftin tact. Region Growing offers several advantages over conventional segmentation techniques. Unlike gradient and Laplacian methods, the borders of regions found by region growing are perfectly thin (since we only add pixels to the exterior of our region) and connected. The algorithm is also very stable with respect to noise.