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2013 (Vol. 5, Issue: 07)
Article Information:

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

Ting Yun and Yiqing Xu
Corresponding Author:  Ting Yun 

Key words:  Gauss mixture model, graph-cut algorithm, mean-shift, ultrasound image, , ,
Vol. 5 , (07): 2458-2465
Submitted Accepted Published
July 26, 2012 September 03, 2012 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.
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  Cite this Reference:
Ting Yun and Yiqing Xu, 2013. Ultrasound Image Segmentation based on the Mean-Shift and Graph Cuts Theory.  Research Journal of Applied Sciences, Engineering and Technology, 5(07): 2458-2465.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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