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     Research Journal of Applied Sciences, Engineering and Technology


Ultrasound Image Segmentation based on the Mean-Shift and Graph Cuts Theory

1, 2Ting Yun and 1, 2Yiqing Xu
1Department of Computer Science, Southeast University, Nanjing, 210096, China
2Department of Information and Science, Nanjing Forestry University, Nanjing, 210027, China
Research Journal of Applied Sciences, Engineering and Technology  2013  7:2458-2465
http://dx.doi.org/10.19026/rjaset.5.4680  |  © The Author(s) 2013
Received: July 26, 2012  |  Accepted: September 03, 2012  |  Published: March 11, 2013

Abstract

This study addressed the issue of vascular ultrasound image segmentation and proposed a novel ultrasonic vascular location and detection method. We contributed in several aspects: Firstly using mean-shift segmentation algorithm to obtain the initial segmentation results of vascular images; Secondly new data item and smooth item of the graph cut energy function was constructed based on the MRF mode, then we put forward swap and α expansion ideas to optimize segmentation results, consequently accurately located the vessel wall and lumen in vascular images. Finally comparison with experts manually tagging results and Appling edge correlation coefficients and variance to verify the validity of our algorithm, experimental results show that our algorithm can efficiently combines the advantages of mean-shift and graph-cut algorithm and achieve better segmentation results.

Keywords:

Gauss mixture model, graph-cut algorithm, mean-shift, ultrasound image,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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