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Segmentation of bone in clinical knee MRI using texture-based geodesic active contours
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00158168openalex-snapshot
Segmenting images using localized histograms and region merging
inferredcites →https://doi.org/10.1007/bf01385685openalex-snapshot
A geometric model for active contours in image processing
inferredcites →https://doi.org/10.1007/bf01427153openalex-snapshot
Region-based strategies for active contour models
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Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
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On active contour models and balloons
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Deformable models in medical image analysis: a survey
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Segmentation of brain tissue from magnetic resonance images
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Boundary finding with parametrically deformable models
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Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
inferredcites →https://doi.org/10.1109/34.632983openalex-snapshot
Prior learning and Gibbs reaction-diffusion
inferredcites →https://doi.org/10.1109/34.824822openalex-snapshot
Medical image analysis: progress over two decades and the challenges ahead
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A geometric snake model for segmentation of medical imagery
inferredcites →https://doi.org/10.1109/cvpr.1996.517146openalex-snapshot
Vector-valued active contours
inferredcites →https://doi.org/10.1109/iccv.1995.466855openalex-snapshot
Gradient flows and geometric active contour models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Deformable Models and Active Contours
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Geodesic Active Contours Applied to Texture Feature Space
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Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research
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