A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building skeleton models via 3-D medial surface/axis thinning algorithms
CVGIP: Graphical Models and Image Processing
Evaluation of Methods for Ridge and Valley Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance-ordered homotopic thinning: a skeletonization algorithm for 3D digital images
Computer Vision and Image Understanding
An augmented Fast Marching Method for computing skeletons and centerlines
VISSYM '02 Proceedings of the symposium on Data Visualisation 2002
International Journal of Computer Vision
Divergence-Based Medial Surfaces
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Statistical Location Model for Abdominal Organ Localization
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Organ pose distribution model and an MAP framework for automated abdominal multi-organ localization
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
The power crust, unions of balls, and the medial axis transform
Computational Geometry: Theory and Applications
Optimal medial surface generation for anatomical volume representations
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
A validation benchmark for assessment of medial surface quality for medical applications
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.