Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images
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Data
2020-08-01
Autores
Jodas, Danilo Samuel
Monteiro da Costa, Maria Francisca
Parreira, Tiago A. A.
Pereira, Aledir Silveira [UNESP]
Tavares, Joao Manuel R. S.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 +/- 0.11, 2.70 +/- 1.69 pixels, 2.79 +/- 1.89 pixels and 3.44 +/- 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.
Descrição
Palavras-chave
Medical imaging, Magnetic resonance imaging, Image segmentation, Snake model, Gray-weighted distance
Como citar
Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 123, 18 p., 2020.