Abdelsamea, Mohammed and Gnecco, Giorgio and Gaber, Mohamed Medhat An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing, 149 (Part B). 820 - 835. ISSN 0925-2312 (2015)
Full text not available from this repository.Abstract
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: | https://doi.org/10.1016/j.neucom.2014.07.052 |
| Uncontrolled Keywords: | Region-based segmentation; Variational level set method; Active contours; Self-organizing neurons; Region-based prior knowledge |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Research Area: | Computer Science and Applications |
| Depositing User: | Ms T. Iannizzi |
| Date Deposited: | 23 Feb 2015 11:04 |
| Last Modified: | 23 Feb 2015 11:04 |
| URI: | http://eprints.imtlucca.it/id/eprint/2620 |
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