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Abstract
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Article Information:
Medical Image Segmentation with Improved Gradient Vector Flow
Jinyong Cheng, Xiaoyun Sun
Corresponding Author: Jinyong Cheng
Submitted: December 20, 2011
Accepted: April 23, 2012
Published: October 15, 2012 |
Abstract:
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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.
Key words: Active contour models , edge detection, gradient vector flow, medical image segmentation, , ,
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Cite this Reference:
Jinyong Cheng, Xiaoyun Sun, . Medical Image Segmentation with Improved Gradient Vector Flow. Research Journal of Applied Sciences, Engineering and Technology, (20): 3951-3957.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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