Image Segmentation Using Watershed Transform
Amandeep Kaur · 2014
Image segmentation is one of the most important categories of image processing. The purpose of image segmentation is to divide an original image into homogeneous regions. It can be applied as a pre-processing stage for other image processing methods. There exist several approaches for image segmentation methods for image processing. The watersheds transformation is studied in this report as a particular method of a region-based approach to the segmentation of an image. First, the basic tool, the watershed transform is defined. It has been shown that it can be implemented by applying flooding process on grey tone image. This flooding process can be performed by using basic morphological operations. The complete transformation incorporates a pre-processing and post-processing stage that deals with embedded problems such as edge ambiguity and the output of a large number of regions. Watershed Transform can be applied to gray scale images, textural images and binary images. The watershed transform has been widely used in many fields of image processing, including medical image segmentation. The image segmentation algorithms are generally based on the two basic characteristics of the luminance: discontinuity and similarity.(1) Edge detection algorithms are based on the discontinuity. Similarly, the threshold processing, region growing, regional separation and polymerization are based on similarity. Watershed algorithm which is a mathematics morphological method for image segmentation based on region processing, has many advantages. The result of watershed algorithm is global segmentation, border closure and high accuracy. It can achieve one-pixel wide, connected, closed and exact location of outline. The basic concept of watershed is based on visualizing a gray level image into its