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On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

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Abdelsamea, Mohammed M., Gnecco, Giorgio, Gaber, Mohamed Medhat and Elyan, Eyad (2015) On the Relationship between Variational Level Set-Based and SOM-Based Active Contours. Computational Intelligence and Neuroscience, 2015 . pp. 1-19. 109029. doi:10.1155/2015/109029 ISSN 1687-5265.

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Official URL: http://dx.doi.org/10.1155/2015/109029

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Abstract

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Cell & Developmental Biology
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Journal or Publication Title: Computational Intelligence and Neuroscience
Publisher: Hindawi Publishing Corporation
ISSN: 1687-5265
Official Date: 2015
Dates:
DateEvent
2015Published
Volume: 2015
Page Range: pp. 1-19
Article Number: 109029
DOI: 10.1155/2015/109029
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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