title: Prior knowledge, random walks and human skeletal muscle segmentation.
abstract: In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.
authors.affiliation: SIEMENS Healthcare, Saint Denis, FR.
authors.forename: P Y
authors.initials: PY
authors.lastname: Baudin
authors.forename: N
authors.initials: N
authors.lastname: Azzabou
authors.forename: P G
authors.initials: PG
authors.lastname: Carlier
authors.forename: Nikos
authors.initials: N
authors.lastname: Paragios
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D018482:Muscle, Skeletal / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011336:Probability; D015203:Reproducibility of Results; D012984:Software