Optimal partitioning methods for image segmentation

Shreyas Fadnavis

Optimal partitioning methods for image segmentation

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Author(s): Shreyas Fadnavis¹
- Affiliations: 1: Computer Engineering , Pune Institute of Computer Technology , Pune , India
Source: Volume 2015, Issue 11, November 2015, p. 341 – 344
DOI: 10.1049/joe.2015.0171 , Online ISSN 2051-3305

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 29/10/2015, Accepted 02/11/2015, Published 10/11/2015

The importance of image processing is increasing in the digitally connected world due to its numerous applications in various fields of medical science, astronomy, weather prediction and video surveillance systems etc. The latest research and development in this field has helped the authors to obtain finer details of a particular image under study. The image segmentation technique, a part of digital image processing, helps to obtain meaningful information of the object. This study discusses the three widely used important image segmentation techniques: namely, split and merge, image growing and thresholding and their effects on a sample image. The authors results thus depict a significant difference in the segmented image by split and merge, image growing and thresholding. Split and merge is the optimal method of image segmentation as compared with the other two techniques mentioned above. The choice of the method varies with type of image, its colour, intensity and noise level.

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