Experimental Comparison of Vasculature Segmentation Methods

Yuchun Ding, Li Bai


Vessel segmentation algorithms play a very important role in vascular disease diagnosis and prediction. Current vessel segmentation research uses mostly images of large vessels, which are relatively easy to extract, but segmenting microvasculature is more challenging and very important for analysing vascular disease such as Alzheimer’s Diseases. The aim of this paper is to report experimental results of several common vessel image segmentation methods. Retinal vessel image database DRIVE is used for 2D experiments and a micro-CT image is used for 3D experiments.


  1. Bedford, L., Hay, D., Devoy, A., Paine, S., Powe, D. G., Seth, R. & Mayer, R. J. (2008). Depletion of 26S proteasomes in mouse brain neurons causes neurodegeneration and Lewy-like inclusions resembling human pale bodies. The Journal of Neuroscience, 28(33), 8189-8198.
  2. Chanwimaluang, T., & Fan, G. (2003, May). An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In Circuits and Systems, 2003. ISCAS'03. Proceedings of the 2003 International Symposium on (Vol. 5, pp. V-21). IEEE.
  3. Daugman, J. G. (1988). Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. Acoustics, Speech and Signal Processing, IEEE Transactions on, 36(7), 1169-1179.
  4. Ding, Y., Ward, W.O.C., Parker, T., Nakagawa, S., Buttery, L., White, L., Bai, L., (2013). Segmentation of Mouse Brain Microvasculature from Micro-CT Images Using Gabor filter and Local Entropy Thresholding, International Symposium on Cerebral Blood Flow, Metabolism and Function.
  5. Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhancement filtering. In Medical Image Computing and Computer-Assisted Interventation-MICCAI'98 (pp. 130-137). Springer Berlin Heidelberg.
  6. Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012). Blood vessel segmentation methodologies in retinal images-A survey. Computer methods and programs in biomedicine.
  7. Lathen, G., Jonasson, J., & Borga, M. (2008). Phase based level set segmentation of blood vessels. In 19th IEEE International Conference on Pattern Recognition.
  8. Lim, P. L., Bagci, U., Bai, L. (2013). Introducing Wilmore Flow into Level Set Segmentation of Spinal Vertebrae, IEEE Transactions on Biomedical Engineering, Vol. 60, No. 1.
  9. Mallat, S., & Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on pattern analysis and machine intelligence, 14(7), 710-732.
  10. Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of computational physics, 79(1), 12-49.
  11. Pal, N. R., & Pal, S. K. (1989). Entropic thresholding. Signal processing, 16(2), 97-108.
  12. Pajak, R. (2003). Use of two-dimensional matched filters for estimating a length of blood vessels newly created in angiogenesis process. Opto-Electronics Review, 11(3), 237-241.
  13. Pujadas, E. R., & Bai, L. (2013). Non-Euclidean basis function based level set segmentation with statistical shape prior, IEEE EMBC2013, Osaka, Japan.
  14. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., & Kikinis, R. (1998). Threedimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical image analysis, 2(2), 143-168.
  15. Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 23(4), 501-509.
  16. Rorden, C., Karnath, H. O., & Bonilha, L. (2007). Improving lesion-symptom mapping. Journal of cognitive neuroscience, 19(7), 1081-1088.
  17. Ward, W., & Bai, L., (2013). Multifractal Analysis of Microvasculature in Health and Diseases, IEEE EMBC2013, Osaka, Japan.

Paper Citation

in Harvard Style

Ding Y. and Bai L. (2014). Experimental Comparison of Vasculature Segmentation Methods . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 425-432. DOI: 10.5220/0004648804250432

in Bibtex Style

author={Yuchun Ding and Li Bai},
title={Experimental Comparison of Vasculature Segmentation Methods},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Experimental Comparison of Vasculature Segmentation Methods
SN - 978-989-758-003-1
AU - Ding Y.
AU - Bai L.
PY - 2014
SP - 425
EP - 432
DO - 10.5220/0004648804250432