title: (Hyper)-graphical models in biomedical image analysis.
abstract: 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.
authors.affiliation: CentraleSupelec, Inria, Université Paris-Saclay, France. Electronic address: nikos.paragios@centralesupelec.fr.
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
authors.affiliation: Imperial College of London, London, UK.
authors.forename: Enzo
authors.initials: E
authors.lastname: Ferrante
authors.affiliation: Imperial College of London, London, UK.
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.affiliation: Ecole des Ponts ParisTech, Université Paris-Est, France.
authors.forename: Nikos
authors.initials: N
authors.lastname: Komodakis
authors.affiliation: Imperial College of London, London, UK.
authors.forename: Sarah
authors.initials: S
authors.lastname: Parisot
authors.affiliation: CentraleSupelec, Inria, Université Paris-Saclay, France.
authors.forename: Evangelia I
authors.initials: EI
authors.lastname: Zacharaki
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated
keywords: (Hyper)graphs; Graph cuts; Image segmentation; Linear programming; Message passing; Random fields; Shape & volume registration