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Mean shift based gradient vector flow for image segmentation

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journal contribution
posted on 04.06.2014, 09:30 authored by Huiyu Zhou, Xuelong Li, Gerald SchaeferGerald Schaefer, M.E. Celebi, Paul Miller
In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.

History

School

  • Science

Department

  • Computer Science

Citation

ZHOU, H. ... et al., 2013. Mean shift based gradient vector flow for image segmentation. Computer Vision and Image Understanding, 117 (9), pp. 1004 - 1016.

Publisher

© Elsevier Inc.

Version

AM (Accepted Manuscript)

Publication date

2013

Notes

This article was published in the journal, Computer Vision and Image Understanding [© Elsevier Inc.] and the definitive version is available at: http://dx.doi.org/10.1016/j.cviu.2012.11.015

ISSN

1077-3142

Language

en