(Hyper)-Graphical Models in Biomedical Image Analysis

Nikos Paragios (1, 2) , Enzo Ferrante (3) , Ben Glocker (3) , Nikos Komodakis (4) , Sarah Parisot (3) , Evangelia I. Zacharaki (1, 2)

1 CVN - Centre de vision numérique
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 Department of Computing [Imperial College London]
4 imagine [Marne-la-Vallée]

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.

Mots clés: Linear Programming; Graph Cuts; Message Passing; (Hyper)Graphs; Random Fields; Image Segmentation; Shape & Volume Registration

Domaines: Imagerie médicale; Traitement des images [eess.IV]; Mathématique discrète [cs.DM]

Nikos Paragios, Enzo Ferrante, Ben Glocker, Nikos Komodakis, Sarah Parisot, et al.. (Hyper)-Graphical Models in Biomedical Image Analysis. Medical Image Analysis, 2016, 33, pp.102-106. ⟨10.1016/j.media.2016.06.028⟩. ⟨hal-01359107⟩