Please use this identifier to cite or link to this item:
https://doi.org/10.1117/12.911649
| Title: | GrowCut-based fast tumor segmentation for 3D magnetic resonance images | Authors: | Yamasaki T. Chen T. Yagi M. Hirai T. Murakami R. |
Keywords: | GrowCut GUI Hierarchical segmentation Magnetic resonance images (MRI) Segmentation Skipping method |
Issue Date: | 2012 | Citation: | Yamasaki T., Chen T., Yagi M., Hirai T., Murakami R. (2012). GrowCut-based fast tumor segmentation for 3D magnetic resonance images. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 8314 : 831434. ScholarBank@NUS Repository. https://doi.org/10.1117/12.911649 | 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. | Source Title: | Progress in Biomedical Optics and Imaging - Proceedings of SPIE | URI: | http://scholarbank.nus.edu.sg/handle/10635/146136 | ISBN: | 9780819489630 | ISSN: | 16057422 | DOI: | 10.1117/12.911649 |
| Appears in Collections: | Staff Publications |
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.