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2012 (Vol. 4, Issue: 20)
Article Information:

Medical Image Segmentation with Improved Gradient Vector Flow

Jinyong Cheng, Xiaoyun Sun
Corresponding Author:  Jinyong Cheng 

Key words:  Active contour models , edge detection, gradient vector flow, medical image segmentation, , ,
Vol. 4 , (20): 3951-3957
Submitted Accepted Published
December 20, 2011 April 23, 2012 October 15, 2012

In this study, we discover some deficiencies of GVF and GGVF Snake such as it can not capture boundaries like “U” and “Ω” completely because of the counteraction of some external forces and the influence of the local minimum external forces. Based on analyzing force distribution rules of gradient vector flow, a standard is introduced to distinguish every control point is true or false. An additional control force is added to GVF Snake model. The direction of control force is gained by tracking the force field and the motion of snake control points. Experimentation proves that the new GVF Snake model can solve the problem that GVF and GGVF Snake model can not detect the boundaries like “U” and “Ω” and the new algorithm can improve GVF snake model’s ability to capture thin boundary indentation like the boundary of brain image.
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  Cite this Reference:
Jinyong Cheng, Xiaoyun Sun, 2012. Medical Image Segmentation with Improved Gradient Vector Flow.  Research Journal of Applied Sciences, Engineering and Technology, 4(20): 3951-3957.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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