Image Segmentation using Watershed Transform
Amandeep Kaur1, Aayushi2
1Amandeep Kaur, Electronics and communication Department, UCOE, Punjabi University, Patiala, India.
2Aayushi, Electronics and communication Department, KITM, Kurukshetra University, Kurukshetra, India.
Manuscript received on March 02, 2014. | Revised Manuscript received on March 05, 2014. | Manuscript published on March 05, 2014. | PP: 5-8 | Volume-4 Issue-1, March 2014. | Retrieval Number: A2060034114/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Flooding, Gradiant, Segmentation, Watershed Transform.