Multi-scale and Snakes for Automatic Road Extraction
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Convergence index filter for vector fields
IEEE Transactions on Image Processing
Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. Intravital microscopy is a powerful tool as it enables in vivoimaging of both vasculature and circulating cells. However, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. In this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. A set of virtual point pairs ("vessel probes") are moved reacting to forces including Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF) forces. Incorporating the cell detection, the VVF force attracts the probes toward the vessel, while the VBVF force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. The vessel probes are moved according to Newtonian Physics reacting to the net of forces applied on them. We demonstrate the results on a set of five real in vivoimages of liver vasculature cluttered by white blood cells. When compared against the ground truth prepared by the technician, the Root Mean Squared Error (RMSE) of segmentation with VVF and VBVF was 55% lower than the method without VVF and VBVF.