image segmentation using geometric deformable models and metaheuristics. Computerized Medical Imaging and Graphics, Elsevier, 2015, 43, pp.167-178. 10.1016/j.compmedimag.2013 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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We introduce a new intensity-based image registration (IR) technique based on a modern, real-coded genetic algorithm. Our proposal is tested on 16 registration scenarios involving real-world MRI medical images. A novel methodological framework to compare heterogeneous IR algorithms is also described. Following such methodology, our algorithm is compared with four well-known IR techniques of different natures. The proposed method is able to improve the results of these techniques in the majority of the scenarios.

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