An improved statistical approach for cerebrovascular tree extraction

Authors:
J. T. Hao;M. L. Li;F. L. Tang
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China
Venue:
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Year:
2006

Citing 5
Cited 0

Muliscale Vessel Enhancement Filtering

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Vessel Extractio Techniques and Algorithms: A Survey

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
A robust Markovian segmentation based on highest confidence first (HCF)

ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

IEEE Transactions on Information Technology in Biomedicine
Bayesian image segmentation using local iso-intensity structural orientation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a statistical approach to aggregating shape and speed information for whole cerebrovascular tree extraction in time-of-flight magnetic resonance angiography (TOF-MRA). By embedding Frangi’s vesselness measure into the prior mopodel, the newly porposede segmentation framework can greatly improve the capability of detecting the tiny vessel branch.