An efficient Self-Organizing Active Contour model for image segmentation

Abdelsamea, Mohammed M. and Gnecco, Giorgio and Gaber, Mohamed Medhat (2015) An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing, 149 (B). pp. 820-835. ISSN 0925-2312

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Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised ACMs are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, effectiveness and efficiency of supervised ACMs are among their main real challenges, especially when handling images containing regions characterized by intensity inhomogeneity. In this paper, to deal with such kinds of images, we propose a new supervised ACM, named Self-Organizing Active Contour (SOAC) model, which combines a variational level set method (a specific kind of ACM) with the weights of the neurons of two Self-Organizing Maps (SOMs). Its main contribution is the development of a new ACM energy functional optimized in such a way that the topological structure of the underlying image intensity distribution is preserved – using the two SOMs – in a parallel-processing and local way. The model has a supervised component since training pixels associated with different regions are assigned to different SOMs. Experimental results show the superior efficiency and effectiveness of SOAC versus several existing ACMs.

Item Type: Article
Identification Number:
Uncontrolled Keywords: Region-based segmentation Variational level set method Active contours Self-organizing neurons Region-based prior knowledge
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 12:01
Last Modified: 22 Mar 2023 12:01

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