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Proceedings Paper

GrowCut-based fast tumor segmentation for 3D magnetic resonance images
Author(s): Toshihiko Yamasaki; Tsuhan Chen; Masakazu Yagi; Toshinori Hirai; Ryuji Murakami
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Paper Abstract

This paper presents a very fast segmentation algorithm based on the region-growing-based segmentation called GrowCut for 3D medical image slices. By the combination of four contributions such as hierarchical segmentation, voxel value quantization, skipping method, and parallelization, the computational time is drastically reduced from 507 seconds to 9.2-14.6 seconds on average for tumor segmentation of 256 x 256 x 200 MRIs.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831434 (14 February 2012); doi: 10.1117/12.911649
Show Author Affiliations
Toshihiko Yamasaki, Cornell Univ. (United States)
The Univ. of Tokyo (Japan)
Japan Society for the Promotion of Science (Japan)
Tsuhan Chen, Cornell Univ. (United States)
Masakazu Yagi, Osaka Univ. (Japan)
Toshinori Hirai, Kumamoto Univ. (Japan)
Ryuji Murakami, Kumamoto Univ. (Japan)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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