Biomedical image segmentation using geometric deformable models and metaheuristics

Pablo Mesejo 1 Andrea Valsecchi 2 Linda Marrakchi-Kacem 3 Stefano Cagnoni 1 Sergio Damas 2
3 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : 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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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, Elsevier, 2015, 43, pp.167-178. ⟨10.1016/j.compmedimag.2013.12.005⟩. ⟨hal-01221316v3⟩

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