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Image Segmentation Using Ant System-Based Clustering Algorithm

  • Aleksandar Jevtić
  • Joel Quintanilla-Domínguez
  • José Miguel Barrón-Adame
  • Diego Andina
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)

Abstract

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Keywords

Image Segmentation Cluster Centre Pheromone Accumulation Pheromone Trail Digital Mammogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aleksandar Jevtić
    • 1
  • Joel Quintanilla-Domínguez
    • 1
  • José Miguel Barrón-Adame
    • 2
  • Diego Andina
    • 1
  1. 1.E.T.S.I. de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain
  2. 2.División de Ingenierias, Campus Irapuato-SalamancaUniversidad de GuanajuatoSalamanca, Gto.Mexico

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