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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: 8 February 2008


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 AlgorithmEvaluation CriterionGround TruthImage SegmentationGood Combination

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Authors’ Affiliations

Laboratoire Terre-Océan, Université de la Polynésie Francaise, Faa'a, Tahiti, Polynésie Française, France
Laboratoire GREYC, ENSICAEN-Université de Caen-CNRS, Caen cedex, France
Institut PRISME, ENSI de Bourges-Université d'Orléans, 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.