Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features.
OBJECTIVE
In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset.


METHODS
First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation.


RESULTS
Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios.


CONCLUSIONS
The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
Ruoxiu
R
Xiao
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
Hui
H
Ding
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
Fangwen
F
Zhai
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
Tong
T
Zhao
Tsinghua University Yuquan Hospital, No. 5, Shijingshan Road, Shijingshan District, Beijing, 100049, China.
Wenjing
W
Zhou
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China. Electronic address: wgz-dea@mail.tsinghua.edu.cn.
Guangzhi
G
Wang
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001499:Bayes Theorem; D001808:Blood Vessels; D002648:Child; D004827:Epilepsy / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D055815:Young Adult
Bayesian classification; Dempster–Shafer evidence theory; Multiscale vascular enhancement; Neurosurgery; Vascular segmentation