On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Learning Probabilistic Models for Contour Completion in Natural Images
International Journal of Computer Vision
Bayesian tracking of elongated structures in 3D images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Joint sulci detection using graphical models and boosted priors
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Quantification of vascular calcifications on digitized mammograms
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
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A fully automatic algorithm is developed for breast arterial calcification extraction in mammograms. This algorithm is implemented in two major steps: a random-walk based tracking step and a compiling and linking step. With given seeds from detected calcification points, the tracking algorithm traverses the vesselness map by exploring the uncertainties of three tracking factors, i.e., traversing direction, jumping distance, and vesselness value, to generate all possible sampling paths. The compiling and linking algorithm further organizes and groups all sampling paths into calcified vessel tracts. The experimental results show that the performance of the proposed automatic calcification extraction algorithm is statistically close to that obtained by manual delineations.