Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

Liver Segmentation with Semi-Supervised Learning

Authors
Li Yonghui, Liu Xiaoxiao
Corresponding Author
Li Yonghui
Available Online December 2015.
DOI
10.2991/nceece-15.2016.63How to use a DOI?
Keywords
Liver Segmentation; Interactive Method; Maximal Similarity; Blocks Merging; Semi-supervised Learning
Abstract

Efficient liver segmentation from volume data provides important assistance for minimal invasive surgery and treatment. However, this task suffers from the special anatomy and topological changes. This paper presents a robust interactive method, which treats it as a semi-supervised learning task. An initial classification is performed to partition the volume data into homogeneous blocks to guide the segmentation. It is easy to implement and a more general linear or nonlinear model can be formed by virtue of semi-supervised learning. Experimental results demonstrate the performance of the proposed scheme in liver contours extracting.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
Series
Advances in Engineering Research
Publication Date
December 2015
ISBN
978-94-6252-150-6
ISSN
2352-5401
DOI
10.2991/nceece-15.2016.63How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Li Yonghui
AU  - Liu Xiaoxiao
PY  - 2015/12
DA  - 2015/12
TI  - Liver Segmentation with Semi-Supervised Learning
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 312
EP  - 319
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.63
DO  - 10.2991/nceece-15.2016.63
ID  - Yonghui2015/12
ER  -