Graph- based segmentation of skeletal striated muscles in NMR images
type: Dataset
contact point: Organization
  fn: CCSD
keyword: theses
keyword: apprentissage-automatique
keyword: machine-learning
keyword: computer-vision
keyword: infoeu-reposemanticsdoctoralthesis
keyword: medical-image-analysis
keyword: vision-par-ordinateur
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: traitement-des-images-medicales
landing page: https://theses.hal.science/tel-00858584
description: Segmentation of magnetic resonance images (MRI) of skeletal striated muscles is of crucial interest when studying myopathies. Diseases understanding, therapeutic followups of patients, etc. rely on discriminating the muscles in MRI anatomical images. However, delineating the muscle contours manually is an extremely long and tedious task, and thus often a bottleneck in clinical research. Typical automatic segmentation methods rely on finding discriminative visual properties between objects of interest, accurate contour detection or clinically interesting anatomical points. Skeletal muscles show none of these features in MRI, making automatic segmentation a challenging problem. In spite of recent advances on segmentation methods, their application in clinical settings is difficult, and most of the times, manual segmentation and correction is still the only option. In this thesis, we propose several approaches for segmenting skeletal muscles automatically in MRI, all related to the popular graph-based Random Walker (RW) segmentation algorithm. The strength of the RW method relies on its robustness in the case of weak contours and its fast and global optimization. Originally, the RW algorithm was developed for interactive segmentation: the user had to pre-segment small regions of the image – called seeds – before running the algorithm which would then complete the segmentation. Our first contribution is a method for automatically generating and labeling all the appropriate seeds, based on a Markov Random Fields formulation integrating prior knowledge of the relative positions, and prior detection of contours between pairs of seeds. A second contribution amounts to incorporating prior knowledge of the shape directly into the RW framework. Such formulation retains the probabilistic interpretation of the RW algorithm and thus allows to compute the segmentation by solving a large but simple sparse linear system, like in the original method. In a third contribution, we propo…
identifier: NNT: 2013ECAP0033
issued: 2026-05-05T20:35:50.172655
language: en
modified: 2026-05-05T20:35:50.172661

test_moissonnage_selune
type: Agent

Graph- based segmentation of skeletal striated muscles in NMR images
type: Distribution
access URL: https://theses.hal.science/tel-00858584
format: HTML
issued: 2026-05-05T20:35:50.194247
modified: 2026-05-05T20:35:50.157685