Model-based detection of tubular structures in 3D images
Computer Vision and Image Understanding
Flux Maximizing Geometric Flows
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
Multiscale detection of curvilinear structures in 2-D and 3-D image data
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Extracting branching object geometry via cores
Extracting branching object geometry via cores
Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Efficient implementation for spherical flux computation and its application to vascular segmentation
IEEE Transactions on Image Processing
Bayesian tracking of tubular structures and its application to carotid arteries in CTA
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Particle filters, a quasi-monte carlo solution for segmentation of coronaries
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Bayesian Maximal Paths for Coronary Artery Segmentation from 3D CT Angiograms
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
A unified minimal path tracking and topology characterization approach for vascular analysis
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Minimum average-cost path for real time 3d coronary artery segmentation of CT images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Hierarchical discriminative framework for detecting tubular structures in 3D images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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In this paper, we present and study two local features for the tracking of vascular structures on 3D angiograms. The first one, Flux, measures the inward gradient flux through circular cross-sections. The second one, MFlux, introduces a non-linear penalization of asymmetric flux contributions to reduce false positive responses. Through a series of experiments on synthetic and real cardiac CT data, we discuss the properties of these features with respect to their parameters. We compare them to a selection of published vessel-dedicated features. We show that MFlux induces a particularly discriminative response landscape, which is a desirable property for tracking purposes on such large search spaces. A key characteristic of the proposed features is their simplicity of implementation and their high computational efficiency, enabling their practical use for advanced tracking strategies.