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Proceedings Paper

A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation
Author(s): Mogana Vadiveloo; Rosni Abdullah; Mandava Rajeswari
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Paper Abstract

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

Paper Details

Date Published: 9 December 2015
PDF: 9 pages
Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170I (9 December 2015); doi: 10.1117/12.2228449
Show Author Affiliations
Mogana Vadiveloo, Univ. Sains Malaysia (Malaysia)
Rosni Abdullah, Univ. Sains Malaysia (Malaysia)
Mandava Rajeswari, Univ. Sains Malaysia (Malaysia)

Published in SPIE Proceedings Vol. 9817:
Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
Yulin Wang; Xudong Jiang, Editor(s)

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