Soft Computing

, Volume 21, Issue 8, pp 2047–2067 | Cite as

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

  • Mohammed M. Abdelsamea
  • Giorgio Gnecco
  • Mohamed Medhat Gaber
Methodologies and Application


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 contour model. 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.


Global region-based segmentation Variational level set method Active contours Chan–Vese model Self-organizing map Neural networks 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mohammed M. Abdelsamea
    • 1
  • Giorgio Gnecco
    • 2
  • Mohamed Medhat Gaber
    • 3
  1. 1.Department of Mathematics Faculty of ScienceUniversity of AssiutAssiutEgypt
  2. 2.IMT Institute for Advanced StudiesLuccaItaly
  3. 3.Robert Gordon UniversityAberdeenUK

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