%0 Journal Article
%@ 0925-2312
%A Abdelsamea, Mohammed
%A Gnecco, Giorgio
%A Gaber, Mohamed Medhat
%D 2015
%F eprints:2620
%I Elsevier
%J Neurocomputing
%K Region-based segmentation; Variational level set method;      Active contours; Self-organizing neurons; Region-based prior knowledge
%N Part B
%P 820 - 835
%T An efficient Self-Organizing Active Contour model for image segmentation 
%U http://eprints.imtlucca.it/2620/
%V 149
%X 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.