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Statistical region-based active contours for segmentation: An overview
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DOI：10.1016/j.irbm.2013.12.002.png)
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Abstract
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In this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method. Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria. (C) 2013 Elsevier Masson SAS. All rights reserved.
Keywords:
LEVEL-SET
IMAGE SEGMENTATION
MINIMIZATION
GRADIENT
TEXTURE
DRIVEN
MOTION
NOISE
MODEL
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