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)

1 Intelligent Bio-Inspired Systems lab (IBISlab)
2 ECSC - European Center for Soft Computing
3 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]

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.

Mots clés: Image Segmentation; Deformable Models; Deformable Registration; Genetic Algorithms; Scatter Search

Domaines: Intelligence artificielle [cs.AI]; Imagerie médicale; Traitement des images [eess.IV]

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-01221316v2⟩