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. 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.
Intelligent Bio-Inspired Systems laboratory (IBISlab), Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy. Electronic address: pmesejo@ce.unipr.it.
Pablo
P
Mesejo
European Center for Soft Computing, C/Gonzalo Gutiérrez Quirós, s/n - 3(a) planta, 33600 Mieres, Spain. Electronic address: andrea.valsecchi@softcomputing.es.
Andrea
A
Valsecchi
Neurospin, CEA, Gif-sur-Yvette, France; CRICM, UPMC Université Paris 6, France. Electronic address: linda.marrakchi@gmail.com.
Linda
L
Marrakchi-Kacem
Intelligent Bio-Inspired Systems laboratory (IBISlab), Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy. Electronic address: cagnoni@ce.unipr.it.
Stefano
S
Cagnoni
European Center for Soft Computing, C/Gonzalo Gutiérrez Quirós, s/n - 3(a) planta, 33600 Mieres, Spain. Electronic address: sergio.damas@softcomputing.es.
Sergio
S
Damas
D000465:Algorithms; D000067508:Computer Heuristics; D003952:Diagnostic Imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*
Deformable models; Deformable registration; Genetic Algorithms; Image segmentation; Scatter Search