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  • Research Article
  • Open Access

Optimization-Based Image Segmentation by Genetic Algorithms

EURASIP Journal on Image and Video Processing20082008:842029

  • Received: 24 June 2007
  • Accepted: 3 February 2008
  • Published:


Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.


  • Genetic Algorithm
  • Evaluation Criterion
  • Ground Truth
  • Image Segmentation
  • Good Combination

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Authors’ Affiliations

Laboratoire Terre-Océan, Université de la Polynésie Francaise, B.P. 6570, 98702 Faa'a, Tahiti, Polynésie Française, France
Laboratoire GREYC, ENSICAEN-Université de Caen-CNRS, 6 Boulevard du Maréchal Juin, 14050 Caen cedex, France
Institut PRISME, ENSI de Bourges-Université d'Orléans, 88 Boulevard Lahitolle, 18020 Bourges cedex, France


© S. Chabrier et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.