Multiple active contours guided by differential evolution for medical image segmentation

I. Cruz-Aceves, J. G. Avina-Cervantes, J. M. Lopez-Hernandez, H. Rostro-Gonzalez, C. H. Garcia-Capulin, M. Torres-Cisneros, R. Guzman-Cabrera

Producció científica: Article en revista indexadaArticleAvaluat per experts

11 Cites (Scopus)


This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

Idioma originalAnglès
Número d’article190304
RevistaComputational and Mathematical Methods in Medicine
Estat de la publicacióPublicada - 2013
Publicat externament


Navegar pels temes de recerca de 'Multiple active contours guided by differential evolution for medical image segmentation'. Junts formen un fingerprint únic.

Com citar-ho