(Hyper)-graphical models in biomedical image analysis
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- 作者：Nikos Paragios ; a ; nikos.paragios@centralesupelec.fr" class="auth_mail" title="E-mail the corresponding author ; Enzo Ferrantec ; Ben Glockerc ; Nikos Komodakisb ; Sarah Parisotc ; Evangelia I. Zacharakia
- 关键词：(Hyper)graphs ; Random fields ; Message passing ; Graph cuts ; Linear programming ; Image segmentation ; Shape & volume registration
- 刊名：Medical Image Analysis
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：33
- 期：Complete
- 页码：102-106
- 全文大小：392 K
文摘
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We briefly present the interest of graphical models in biomedical imaging.
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We present/briefly review the state of the art optimization algorithms for their inference.
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We provide a snapshot of methods of increasing complexity/richness to address core biomedical imaging problems.
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We provide some insights as it concerns future developments and impact of these methods for the decade to come.