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Article Dans Une Revue Computerized Medical Imaging and Graphics Année : 2015

Biomedical image segmentation using geometric deformable models and metaheuristics

Résumé

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities.
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Commentaire Segmentation framework used in the paper

Dates et versions

hal-01221316 , version 1 (04-03-2016)
hal-01221316 , version 2 (07-03-2016)
hal-01221316 , version 3 (08-03-2016)

Identifiants

Citer

Pablo Mesejo, Andrea Valsecchi, Linda Marrakchi-Kacem, Stefano Cagnoni, Sergio Damas. Biomedical image segmentation using geometric deformable models and metaheuristics. Computerized Medical Imaging and Graphics, 2015, 43, pp.167-178. ⟨10.1016/j.compmedimag.2013.12.005⟩. ⟨hal-01221316v1⟩
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