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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/14739

Title: Mean shift based gradient vector flow for image segmentation
Authors: Zhou, Huiyu
Li, Xuelong
Schaefer, Gerald
Celebi, M.E.
Miller, Paul
Keywords: Image segmentation
Mean shift
Gradient vector flow
Energy function
Issue Date: 2013
Publisher: © Elsevier Inc.
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.
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1016/j.cviu.2012.11.015
URI: https://dspace.lboro.ac.uk/2134/14739
Publisher Link: http://dx.doi.org/10.1016/j.cviu.2012.11.015
ISSN: 1077-3142
Appears in Collections:Published Articles (Computer Science)

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