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Proceedings Paper

Real-time 3D medical structure segmentation using fast evolving active contours
Author(s): Xiaotao Wang; Qiang Wang; Zhihui Hao; Kuanhong Xu; Ping Guo; Haibing Ren; Wooyoung Jang; Jung-bae Kim
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Paper Abstract

Segmentation of 3D medical structures in real-time is an important as well as intractable problem for clinical applications due to the high computation and memory cost. We propose a novel fast evolving active contour model in this paper to reduce the requirements of computation and memory. The basic idea is to evolve the brief represented dynamic contour interface as far as possible per iteration. Our method encodes zero level set via a single unordered list, and evolves the list recursively by adding activated adjacent neighbors to its end, resulting in active parts of the zero level set moves far enough per iteration along with list scanning. To guarantee the robustness of this process, a new approximation of curvature for integer valued level set is proposed as the internal force to penalize the list smoothness and restrain the list continual growth. Besides, list scanning times are also used as an upper hard constraint to control the list growing. Together with the internal force, efficient regional and constrained external forces, whose computations are only performed along the unordered list, are also provided to attract the list toward object boundaries. Specially, our model calculates regional force only in a narrowband outside the zero level set and can efficiently segment multiple regions simultaneously as well as handle the background with multiple components. Compared with state-of-the-art algorithms, our algorithm is one-order of magnitude faster with similar segmentation accuracy and can achieve real-time performance for the segmentation of 3D medical structures on a standard PC.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903443 (21 March 2014); doi: 10.1117/12.2042974
Show Author Affiliations
Xiaotao Wang, Samsung Advanced Institute of Technology (China)
Qiang Wang, Samsung Advanced Institute of Technology (China)
Zhihui Hao, Samsung Advanced Institute of Technology (China)
Kuanhong Xu, Samsung Advanced Institute of Technology (China)
Ping Guo, Samsung Advanced Institute of Technology (China)
Haibing Ren, Samsung Advanced Institute of Technology (China)
Wooyoung Jang, Samsung Advanced Institute of Technology (Korea, Republic of)
Jung-bae Kim, Samsung Advanced Institute of Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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