Automatic Initialization Active Contour Model for the Segmentation of the Chest Wall on Chest CT.
- Author:
Seokyoon CHOI
1
;
Changsoo KIM
Author Information
- Publication Type:Original Article
- Keywords: Active Contour Model; Automatic Initialization; Mean Shape; Gradient Vector Flow; Computed Tomography
- MeSH: Diffusion; Snakes; Thoracic Wall; Thorax; Vision, Ocular
- From:Healthcare Informatics Research 2010;16(1):36-45
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: Snake or active contours are extensively used in computer vision and medical image processing applications, and particularly to locate object boundaries. Yet problems associated with initialization and the poor convergence to boundary concavities have limited their utility. The new method of external force for active contours, which is called gradient vector flow (GVF), was recently introduced to address the problems. METHODS: This paper presents an automatic initialization value of the snake algorithm for the segmentation of the chest wall. Snake algorithms are required to have manually drawn initial contours, so this needs automatic initialization. In this paper, our proposed algorithm is the mean shape for automatic initialization in the GVF. RESULTS: The GVF is calculated as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the medical images. Finally, the mean shape coordinates are used to automatic initialize thepoint of the snake. The proposed algorithm is composed of three phases: the landmark phase, the procrustes shape distance metric phase and aligning a set of shapes phase. The experiments showed the good performance of our algorithm in segmenting the chest wall by chest computed tomography. CONCLUSIONS: An error analysis for the active contours results on simulated test medical images is also presented. We showed that GVF has a large capture range and it is able to move a snake into boundary concavities. Therefore, the suggested algorithm is better than the traditional potential forces of image segmentation.
