Automatic skeletal muscle segmentation through random walks and graph-based seed placement

Pierre-Yves Baudin (1, 2) , Noura Azzabou (3, 4, 5) , Pierre G. Carlier (3, 4, 5) , Nikos Paragios (2, 6, 7, 8, 9, 10)

1 CVN - Centre de vision numérique
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 Institut de Myologie
4 MIRCEN - Molecular Imaging Research Center [Fontenay-aux-Roses]
5 UPMC - Université Pierre et Marie Curie - Paris 6
6 imagine [Marne-la-Vallée]
7 LIGM - Laboratoire d'Informatique Gaspard-Monge
8 UPEM - Université Paris-Est Marne-la-Vallée
9 ENPC - École des Ponts ParisTech
10 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

In this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome of this optimization process is fed to a powerful graphbased diffusion segmentation method (random walker) that is able to produce very promising results through a fully automated approach. Validation on challenging data sets demonstrates the potentials of our method.

Mots clés: muscle; image segmentation; graphical models

Domaines: Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Pierre-Yves Baudin, Noura Azzabou, Pierre G. Carlier, Nikos Paragios. Automatic skeletal muscle segmentation through random walks and graph-based seed placement. International Symposium Biomedical Imaging (ISBI), May 2012, Barcelone, Spain. pp.1036--1039, ⟨10.1109/isbi.2012.6235735⟩. ⟨hal-00773616⟩