Graph- based segmentation of skeletal striated muscles in NMR images
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Summary
This thesis proposes several approaches for segmenting skeletal muscles automatically in MRI, all related to the popular graph-based Random Walker (RW) segmentation algorithm, and proposes a learning framework to estimate the optimal set of parameters for balancing the contrast term of the RW algorithm and the different existing prior models.
- Type
- preprint
- Published
- 2013-05-23
- Cited by
- 6
- References
- 118
- Access
- Open access
- OpenAlex
- https://openalex.org/W4567138
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59734374
Keywords
Segmentation, Artificial intelligence, Computer science, Robustness (evolution), Discriminative model
References
- Modélisation, déformation et reconnaissance d'objets tridimensionnels à l'aide de maillages simplexes
- Prior Knowledge, Random Walks and Human Skeletal Muscle Segmentation
- Probabilistic Multi-shape Segmentation of Knee Extensor and Flexor Muscles
- Image segmentation using MRFs and statistical shape modeling
- A Random Walks View of Spectral Segmentation
- Handbook of Mathematical Models in Computer Vision
- Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
- Muscle Imaging in Health and Disease
- A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting
- Efficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation
- Oriented particles: A tool for shape memory objects modelling
- 3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs
- Deformable M-Reps for 3D Medical Image Segmentation
- General Object Reconstruction Based on Simplex Meshes
- Fast Musculoskeletal Registration Based on Shape Matching
- Fast segmentation, tracking, and analysis of deformable objects
- A PDE-based level-set approach for detection and tracking of moving objects
- A kernel view of the dimensionality reduction of manifolds
- Automatic segmentation of the colon for virtual colonoscopy.
- A review of deformable surfaces: topology, geometry and deformation
Cited by
- Fully Automated Medical Image Analysis Facilitating Subsequent User Analysis
- The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation
- Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
- Deep Learning-Based Automatic Pipeline for Quantitative Assessment of Thigh Muscle Morphology and Fatty Infiltration
- Prostate Segmentation with Random Walker by Automatic Seed Generation
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