Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Reconstructing coronary arterial segments from three projection boundaries
Pattern Recognition Letters
Pattern Recognition Letters
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II
IEEE Transactions on Information Technology in Biomedicine
Automatic segmentation of age-related macular degeneration in retinal fundus images
Computers in Biology and Medicine
An automatic diagnosis method for the knee meniscus tears in MR images
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Computer Methods and Programs in Biomedicine
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In this paper a fully automatic method is presented for extracting blood vessel structures in poor quality coronary angiograms. The method extracts blood vessels by exploiting the spatial coherence in the image. Accurate sampling of a blood vessel requires a background elimination technique. A circular sampling technique is employed to exploit the coherence. This circular sampling technique is also applied to determine the distribution of intersection lengths between the circles and blood vessels at various threshold depths. After this sampling process, disconnected parts to the centered object are eliminated, and then the distribution of the intersection length is examined to make the decision about whether the point is on the blood vessel. To produce the final segmented image, mis-segmented noisy parts and discontinuous parts are eliminated by using angle couples and circular filtering techniques. The performance of the method is examined on various poor quality X-ray angiogram images.