CEASAR: a smooth, accurate and robust centerline extraction algorithm
Proceedings of the conference on Visualization '00
Penalized-Distance Volumetric Skeleton Algorithm
IEEE Transactions on Visualization and Computer Graphics
Segmentation of Cerebral Vessels and Aneurysms from MR Angiography Data
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Intensity Ridge and Widths for Tubular Object Segmentation and Description
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Curve-Skeleton Properties, Applications, and Algorithms
IEEE Transactions on Visualization and Computer Graphics
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We discuss in this paper the problem of localizing and quantifying intracranial aneurysms. Assuming that the segmentation of medical images is done, and that a 3D representation of the vascular tree is available, we present a new automatic algorithm to extract vessels centerlines. Aneurysms are then automatically detected by studying variations of vessels diameters. Once an aneurysm is detected, we give measures that are important to decide its treatment. The name of the aneurysm-carrying vessel is computed using an inexact graph matching technique. The proposed approach is evaluated on segmented real images issued from Magnetic Resonance Angiography (MRA) and CT scan.