Handbook of medical imaging
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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Vascular disease is characterized by any condition that affects the circulatory system. Recently, a demand for sophisticated software tools that can characterize the integrity and functional state of vascular networks from different vascular imaging modalities has appeared. Such tools face significant challenges such as: large datasets, similarity in intensity distributions of other organs and structures, and the presence of complex vessel geometry and branching patterns. Towards that goal, this paper presents a new approach to automatically track vascular networks from CTA and MRA images. Our methodology is based on the Hough transform to dynamically estimate the centerline and vessel diameter along the vessel trajectory. Furthermore, the vessel architecture and orientation is determined by the analysis of the Hessian matrix of the CTA or MRA intensity distribution. Results are shown using both synthetic vessel datasets and real human CTA and MRA images. The tracking algorithm yielded high reproducibility rates, robustness to different noise levels, associated with simplicity of execution, which demonstrates the feasibility of our approach.