Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
503987 | Computerized Medical Imaging and Graphics | 2015 | 12 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Pablo Mesejo, Andrea Valsecchi, Linda Marrakchi-Kacem, Stefano Cagnoni, Sergio Damas,