Robust segmentation of cerebral arterial segments by a sequential Monte Carlo method: particle filtering.
In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence. A particle filter is employed as the main framework for tracking and is equipped with adaptive properties to both bone contact and vein contamination. The proposed tracking method is evaluated by the experiments on both synthetic and actual data. A variety of vessels were synthesized to assess the sensitivity to the axis curvature change, obscure boundaries, and noise. The experimental results showed that the proposed method is also insensitive to parameter settings and requires less user intervention than the conventional vessel tracking methods, which proves its improved robustness.
Shim
Hackjoon
H
School of Electrical Engineering and Computer Science, Seoul National University, Seoul 151-742, Republic of Korea. hjshim@diehard.snu.ac.kr
Kwon
Dongjin
D
Yun
Il Dong
ID
Lee
Sang Uk
SU
D002533:Cerebral Angiography; D002536:Cerebral Arteries / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009010:Monte Carlo Method; D014057:Tomography, X-Ray Computed