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(Hyper)-graphical models in biomedical image analysis
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DOI：10.1016/j.media.2016.06.028.png)
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Abstract
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Computational vision, visual computing and biomedical image analysis have made tremendous progress over the past two decades. This is mostly due the development of efficient learning and inference algorithms which allow better and richer modeling of image and visual understanding tasks. Hyper-graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the importance of such representations, discuss their strength and limitations, provide appropriate strategies for their inference and present their application to address a variety of problems in biomedical image analysis. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.
Keywords:
(Hyper)graphs
Random fields
Message passing
Graph cuts
Linear programming
Image segmentation
Shape & volume registration
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