Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

Segmentation of Ultrasonic Images Based on Modified Chan-Vese algorithm

Authors
Liqun Wang, Honghui Fan
Corresponding Author
Liqun Wang
Available Online April 2016.
DOI
https://doi.org/10.2991/emim-16.2016.147How to use a DOI?
Keywords
Ultrasonic image segmentation; Chan-Vese model; Level set method; Image process
Abstract
The primary ultrasonic image segmentation goal is to partition a given ultrasonic image into different regions representing anatomical structures. Ultrasonic image segmentation is very important because the accurate representation of object and background ?uid provides a way to identify many disease. In this paper, a modified Chan-Vese model is proposed for image segmentation, which is based on the similarity between each point and center point in the neighborhood. This model can capture the details of local region to realize the image segmentation in gray-level heterogeneous area. Experimental results show that this method can segment the ultrasonic image with high accuracy, adapt ability and more stable performance compared with the traditional Chan-Vese model.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Conference on Electronic, Mechanical, Information and Management Society
Part of series
Advances in Computer Science Research
Publication Date
April 2016
ISBN
978-94-6252-176-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/emim-16.2016.147How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Liqun Wang
AU  - Honghui Fan
PY  - 2016/04
DA  - 2016/04
TI  - Segmentation of Ultrasonic Images Based on Modified Chan-Vese algorithm
BT  - 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 710
EP  - 714
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.147
DO  - https://doi.org/10.2991/emim-16.2016.147
ID  - Wang2016/04
ER  -