This paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed.

Automatic evolutionary medical image segmentation using deformable models / Valsecchi, Andrea; MESEJO SANTIAGO, Pablo; Marrakchi Kacem, Linda; Cagnoni, Stefano; Damas, Sergio. - CD-ROM. - (2014), pp. 97-104. (Intervento presentato al convegno 2014 IEEE Congress on Evolutionary Computation, CEC 2014 tenutosi a chn nel 2014) [10.1109/CEC.2014.6900466].

Automatic evolutionary medical image segmentation using deformable models

MESEJO SANTIAGO, Pablo;CAGNONI, Stefano;
2014-01-01

Abstract

This paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed.
2014
9781479914883
9781479914883
Automatic evolutionary medical image segmentation using deformable models / Valsecchi, Andrea; MESEJO SANTIAGO, Pablo; Marrakchi Kacem, Linda; Cagnoni, Stefano; Damas, Sergio. - CD-ROM. - (2014), pp. 97-104. (Intervento presentato al convegno 2014 IEEE Congress on Evolutionary Computation, CEC 2014 tenutosi a chn nel 2014) [10.1109/CEC.2014.6900466].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2813491
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