Partition-Based extraction of cerebral arteries from CT angiography with emphasis on adaptive tracking

Authors:
Hackjoon Shim;Il Dong Yun;Kyoung Mu Lee;Sang Uk Lee
Affiliations:
School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea;School of Electronics and Information Engineering, Hankuk University of Foreign Studies, Yongin, Korea;School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea;School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea
Venue:
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Year:
2005

Citing 0
Cited 3

Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: Particle filtering

Computer Methods and Programs in Biomedicine
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
Toward interactive user guiding vessel axis extraction from gray-scale angiograms: an optimization framework

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a method to extract cerebral arteries from computed tomographic angiography (CTA) is proposed. Since CTA shows both bone and vessels, the examination of vessels is a difficult task. In the upper part of the brain, the arteries of main interest are not close to bone and can be well segmented out by thresholding and simple connected-component analysis. However in the lower part the separation is challenging due to the spatial closeness of bone and vessels and their overlapping intensity distributions. In this paper a CTA volume is partitioned into two sub-volumes according to the spatial relationship between bone and vessels. In the lower sub-volume, the concerning arteries are extracted by tracking the center line and detecting the border on each cross-section. The proposed tracking method can be characterized by the adaptive properties to the case of cerebral arteries in CTA. These properties improve the tracking continuity with less user-interaction.