Biomedical image segmentation using geometric deformable models and metaheuristics

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.
Source: Computerized Medical Imaging and Graphics - Category: Radiology Authors: Source Type: research