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Proceedings Paper

Level-set based vessel segmentation accelerated with periodic monotonic speed function
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Paper Abstract

To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour's local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points' expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.

Paper Details

Date Published: 11 March 2011
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621M (11 March 2011); doi: 10.1117/12.876704
Show Author Affiliations
Chunliang Wang, Linköping Univ. (Sweden)
Hans Frimmel, Uppsala Univ. (Sweden)
Örjan Smedby, Linköping Univ. (Sweden)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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