Full Text:   <534>

Summary:  <114>

CLC number: TP391.4

On-line Access: 2019-08-29

Received: 2018-03-02

Revision Accepted: 2018-07-31

Crosschecked: 2019-08-15

Cited: 0

Clicked: 1299

Citations:  Bibtex RefMan EndNote GB/T7714


Shi-feng Zhao


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.8 P.1099-1108


Vascular segmentation of neuroimages based on a prior shape and local statistics

Author(s):  Yun Tian, Zi-feng Liu, Shi-feng Zhao

Affiliation(s):  College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; more

Corresponding email(s):   zhao_shifeng@bnu.edu.cn

Key Words:  Vesselness filter, Neighborhood, Blood-vessel segmentation, Outlier

Yun Tian, Zi-feng Liu, Shi-feng Zhao. Vascular segmentation of neuroimages based on a prior shape and local statistics[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1099-1108.

@article{title="Vascular segmentation of neuroimages based on a prior shape and local statistics",
author="Yun Tian, Zi-feng Liu, Shi-feng Zhao",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Vascular segmentation of neuroimages based on a prior shape and local statistics
%A Yun Tian
%A Zi-feng Liu
%A Shi-feng Zhao
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 8
%P 1099-1108
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800129

T1 - Vascular segmentation of neuroimages based on a prior shape and local statistics
A1 - Yun Tian
A1 - Zi-feng Liu
A1 - Shi-feng Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 8
SP - 1099
EP - 1108
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800129

Fast and accurate extraction of vascular structures from medical images is fundamental for many clinical procedures. However, most of the vessel segmentation techniques ignore the existence of the isolated and redundant points in the segmentation results. In this study, we propose a vascular segmentation method based on a prior shape and local statistics. It could efficiently eliminate outliers and accurately segment thick and thin vessels. First, an improved vesselness filter is defined. This quantifies the likelihood of each voxel belonging to a bright tubular-shaped structure. A matching and connection process is then performed to obtain a blood-vessel mask. Finally, the region-growing method based on local statistics is implemented on the vessel mask to obtain the whole vascular tree without outliers. Experiments and comparisons with Frangi’s and Yang’s models on real magnetic- resonance-angiography images demonstrate that the proposed method can remove outliers while preserving the connectivity of vessel branches.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Adel M, Moussaoui A, Rasigni M, et al., 2010. Statistical- based tracking technique for linear structures detection: application to vessel segmentation in medical images. IEEE Signal Process Lett, 17(6):555-558.

[2]Becker C, Rigamonti R, Lepetit V, et al., 2013. Supervised feature learning for curvilinear structure segmentation. Int Conf on Medical Image Computing and Computer- Assisted Intervention, p.526-533.

[3]Bogunović H, Pozo JM, Villa-Uriol MC, et al., 2011. Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF-MRA using geodesic active regions: an evaluation study. Med Phys, 38(1):210-222.

[4]Chan MY, Wu YC, Qu HM, et al., 2006. MIP-guided vascular image visualization with multi-dimensional transfer function. 24th Computer Graphics Int Conf, p.372-384.

[5]Chen L, Mossa-Basha M, Balu N, et al., 2018. Development of a quantitative intracranial vascular features extraction tool on 3DMRA using semiautomated open-curve active contour vessel tracing. Magn Reson Med, 79(6):3229- 3238.

[6]Chowriappa A, Kesavadas T, Mokin M, et al., 2013. Vascular decomposition using weighted approximate convex decomposition. Int J Comput Assist Radiol Surg, 8(2):207- 219.

[7]Ding Y, Ward WOC, Wästerlid T, et al., 2014. Three- dimensional vessel segmentation using a novel combinatory filter framework. Phys Med Biol, 59(22):7013-7029.

[8]El-Baz A, Farag AA, Gimel’farb G, et al., 2005. Automatic cerebrovascular segmentation by accurate probabilistic modeling of TOF-MRA images. Int Conf on Medical Image Computing and Computer-Assisted Intervention, p.34-42.

[9]El-Baz A, Elnakib A, Khalifa F, et al., 2012. Precise segmentation of 3-D magnetic resonance angiography. IEEE Trans Biomed Eng, 59(7):2019-2029.

[10]Forkert ND, Schmidt-Richberg A, Fiehler J, et al., 2013. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imag, 31(2):262-271.

[11]Frangi AF, Niessen WJ, Vincken KL, et al., 1998. Multiscale vessel enhancement filtering. 1st Int Conf on Medical Image Computing and Computer-Assisted Intervention, p.130-137.

[12]Hannink J, Duits R, Bekkers E, 2014. Vesselness via multiple scale orientation scores. https://arxiv.org/abs/1402.4963

[13]Hibet-Allah O, Hajer J, Kamel H, 2016. Vascular tree segmentation in MRA images using Hessian-based multiscale filtering and local entropy thresholding. 2nd Int Conf on Advanced Technologies for Signal and Image Processing, p.325-329.

[14]Kumar RP, Albregtsen F, Reimers M, et al., 2015. Blood vessel segmentation and centerline tracking using local structure analysis. 6th European Conf of the Int Federation for Medical and Biological Engineering, p.122-125.

[15]Rajeswari J, Jagannath M, 2017. Advances in biomedical signal and image processing—a systematic review. Inform Med Unlocked, 8:13-19.

[16]Sulayman N, Al-Mawaldi M, Kanafani Q, 2016. Semi- automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images. Egypt J Radiol Nucl Med, 47(3):859-865.

[17]Tian Y, Duan FQ, Lu K, et al., 2013. A flexible 3D cerebrovascular extraction from TOF-MRA images. Neurocomputing, 121:392-400.

[18]Wang R, Li C, Wang J, et al., 2015. Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images. J Neurosci Methods, 241:30-36.

[19]Woźniak T, Strzelecki M, 2015. Segmentation of 3D magnetic resonance brain vessel images based on level set approaches. Signal Processing: Algorithms, Architectures, Arrangements, and Applications, p.56-61.

[20]Yang GY, Kitslaar P, Frenay M, et al., 2012. Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int J Cardiov Imag, 28(4):921-933.

[21]Yang JZ, Ma S, Sun Q, et al., 2014. Improved Hessian multiscale enhancement filter. Biomed Mater Eng, 24(6): 3267-3275.

[22]Yang X, Cheng KTT, Chien A, 2014. Accurate vessel segmentation with progressive contrast enhancement and Canny refinement. Asian Conf on Computer Vision, p.1-16.

[23]Zhao SF, Zhou MQ, Jia TR, et al., 2014. Multi-branched cerebrovascular segmentation based on phase-field and likelihood model. Comput Graph, 38:239-247.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE