A SOM-based Chan–Vese model for unsupervised image segmentation

Abdelsamea, Mohammed M. and Gnecco, Giorgio and Gaber, Mohamed Medhat (2015) A SOM-based Chan–Vese model for unsupervised image segmentation. Soft Computing. pp. 1-21. ISSN 1432-7643

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Active Contour Models (ACMs) constitute an
efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling images that contain objects characterized by many different intensities still represents a challenge. In this paper, we propose a novel ACM that combines—in a global and unsupervised way—the advantages of the Self-Organizing Map (SOM) within the level set framework of a state-of-the-art unsupervised global ACM, the Chan–Vese (C–V) model. We term our proposed model SOM-based Chan– Vese (SOMCV) active contourmodel. It works by explicitly integrating the global information coming from the weights (prototypes) of the neurons in a trained SOM to help choosing whether to shrink or expand the current contour during the optimization process, which is performed in an iterative
way. The proposed model can handle images that contain
objects characterized by complex intensity distributions, and is at the same time robust to the additive noise. Experimental results show the high accuracy of the segmentation results obtained by the SOMCV model on several synthetic and real images, when compared to the Chan–Vese model and other image segmentation models.

Item Type: Article
Identification Number: https://doi.org/10.1007/s00500-015-1906-z
29 October 2015Published
Uncontrolled Keywords: Global region-based segmentation · Variational level set method · Active contours · Chan–Vese model · Self-organizing map · Neural networks
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Ian Mcdonald
Date Deposited: 26 Jan 2017 11:38
Last Modified: 22 Mar 2023 12:01
URI: https://www.open-access.bcu.ac.uk/id/eprint/3828

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