Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lidar waveform modeling using a marked point process
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A marked point process for modeling lidar waveforms
IEEE Transactions on Image Processing
Reconstructing geometrically consistent tree structures from noisy images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Efficient monte carlo sampler for detecting parametric objects in large scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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This paper presents a two-step algorithm to perform automatic extraction of vessel tree on angiogram. Firstly, the approximate vessel centerline is modeled as marked point process with each point denoting a line segment. A Double Area prior model is proposed to incorporate the geometrical and topological constraints of segments through potentials on the interaction and the type of segments. Data likelihood allows for the vesselness of the points which the segment covers, which is computed through the Hessian matrix of the image convolved with 2-D Gaussian filter at multiple scales. Optimization is realized by simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. Secondly, the extracted approximate vessel centerline, containing global geometry shape as well as location information of vessel, is used as important guide to explore the accurate vessel edges by combination with local gradient information of angiogram. This is implemented by morphological homotopy modification and watershed transform on the original gradient image. Experimental results of clinical digitized coronary angiogram are reported.