IEICE TRANSACTIONS on Information
A Hybrid Approach via SRG and IDE for Volume Segmentation
Summary :
Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2257-2260
- Publication Date
- 2017/09/01
- Publicized
- 2017/06/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8085
- Type of Manuscript
- LETTER
- Category
- Computer Graphics
Authors
Li WANG
National University of Defense Technology
Xiaoan TANG
National University of Defense Technology
Junda ZHANG
National University of Defense Technology
Dongdong GUAN
National University of Defense Technology
Keyword
Latest Issue
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Li WANG, Xiaoan TANG, Junda ZHANG, Dongdong GUAN, "A Hybrid Approach via SRG and IDE for Volume Segmentation" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2257-2260, September 2017, doi: 10.1587/transinf.2017EDL8085.
Abstract: Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8085/_p
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@ARTICLE{e100-d_9_2257,
author={Li WANG, Xiaoan TANG, Junda ZHANG, Dongdong GUAN, },
journal={IEICE TRANSACTIONS on Information},
title={A Hybrid Approach via SRG and IDE for Volume Segmentation},
year={2017},
volume={E100-D},
number={9},
pages={2257-2260},
abstract={Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.},
keywords={},
doi={10.1587/transinf.2017EDL8085},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - A Hybrid Approach via SRG and IDE for Volume Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 2257
EP - 2260
AU - Li WANG
AU - Xiaoan TANG
AU - Junda ZHANG
AU - Dongdong GUAN
PY - 2017
DO - 10.1587/transinf.2017EDL8085
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.
ER -
English
