Extraction of Vascular Structure in 3D Cardiac CT Images by Using Object/Background Normalization


The vessel structures of the blood circulatory system are one of the most complex structures of the human body. Modern computed tomography techniques allow acquiring high resolution images, but at the same time, the number of artifacts in output images is quite high. They may affect diagnostic result and may obscure or simulate pathology. The idea of our method is to represent a 3D computed tomography image as a combination of vascular structure and background that has normal distribution in some neighborhood. Locally adaptive non-linear filters decrease global difference between bright and dark voxels, even if it produces better local contrast. Luminosity and contrast are observed from image background and are used for normalization of the whole image. After making background normalization at each layer, we merge layers and reconstruct vessels structure. The proposed method has been tested on real cardiac CT images, the test results show that high quality 3D structures are reconstructed, without requiring a priori knowledge or user interaction. The tested dataset has been made publicly available. The proposed approach can be applied to denoising computed tomography images, enhancing of contrast in lesion areas without changing topology of initial vessel structures.

This is a preview of subscription content, log in to check access.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.


  1. 1

    The National Statistical Committee of the Republic of Belarus, “Demographic Yearbook of the Republic of Belarus,” 323 p. (2003).

  2. 2

    World Health Organization, “The top 10 causes of death.” http://www.who.int/mediacentre/factsheets/fs310/en/

  3. 3

    F. E. Boas and D. Fleischmann, “CT artifacts: Causes and reduction techniques,” Imaging Med. 4 (2), 229–240 (2012).

    Article  Google Scholar 

  4. 4

    C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv. 36 (2), 81–121 (2004).

    Article  Google Scholar 

  5. 5

    D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, “A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes,” Med. Image Anal. 13 (6), 819–845 (2009).

    Article  Google Scholar 

  6. 6

    E. Bullitt and S. R. Aylward, “Analysis of time-varying images using 3d vascular models,” in Proc. 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery (Washington, DC, USA, 2001), IEEE, pp. 9–14.

  7. 7

    A. M. Yatchenko, A. S. Krylov, A. V. Gavrilov, and I. V. Arkhipov, “3D liver vessels model design using CT data,” in Proc. 19th Int. Conf. on Computer Graphics and Vision (GraphiCon2009) (Moscow, Russia, 2009), pp. 344−347 [in Russian].

  8. 8

    R. Grothausmann, M. Kellner, M. Heidrich, et al., “Method for 3D airway topology extraction,” Comput. Math. Methods Med. 2015, Article ID 127010 (2015). https://doi.org/10.1155/2015/127010

    Article  Google Scholar 

  9. 9

    J. F. Carrillo, M. Orkisz, and M. Hernández Hoyos, “Extraction of 3D vascular tree skeletons based on the analysis of connected components evolution,” in Computer Analysis of Images and Patterns, Proc. 11th Int. Conf. CAIP 2005 (Versailles, France, 2005), Ed. by A. Gagalowicz and W. Philips, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2005), Vol. 3691, pp. 604–611.

  10. 10

    G. Yang, P. Kitslaar, M. Frenay, et al., “Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography,” Int. J. Cardiovasc. Imaging 28 (4), 921–933 (2012).

    Article  Google Scholar 

  11. 11

    R. Bates, B. Irving, B. Markelc, et al., “Extracting 3D vascular structures from microscopy images using Convolutional Recurrent Networks,” arXiv preprint arXiv:1705.09597 (2017). https://arxiv.org/abs/1705.09597

  12. 12

    H. S. Bhadauria, S. S. Bisht, and A. Singh, “Vessels extraction from retinal images,” IOSR J. Electron. Commun. Eng. 6 (3), 79–82 (2013).

    Article  Google Scholar 

  13. 13

    Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30 (8), 2040–2051 (2003).

    Article  Google Scholar 

  14. 14

    C. T. Metz, M. Schaap, A. C. Weustink, et al., “Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach,” Med. Phys. 36 (12), 5568–5579 (2009).

    Article  Google Scholar 

  15. 15

    D. Hancharou, A. Nedzved, and S. Ablameyko, “3D Distance transform and its application for processing of medical images,” J. Inf., Control Manage. Syst. 8 (2), 43–53 (2010).

    Google Scholar 

  16. 16

    D. Hancharou, A. Nedzved, and S. Ablameyko, “Skeletonization algorithm of high resolution vascular data,” in Pattern Recognition and Information Processing (PRIP2014), Proc. 12th Int. Conf. (Minsk, Belarus, 2014), pp. 76–80.

Download references


This work was supported by Public Welfare Technology Applied Research Program of Zhejiang Province (grant nos. LGJ18F020001, LGF19F020016 and LGJ19F020002), Zhejiang Provincial Natural Science Foundation of China (grant no. LZ15F020001), Belarusian Republican Foundation for Foundational Research (grant no. F16R-180), and the National High-end Foreign Experts Program (grant no. GDW20163300034).

Author information



Corresponding authors

Correspondence to S. Ye or D. Hancharou or H. Chen or A. Nedzvedz or H. Lv or S. Ablameyko.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Shiping Ye. Born in 1967. Professor and Vice President of Zhejiang Shuren University. Graduated from Zhejiang University in 1988. In 2003 he got his master’s degree in Computer Science and Technology from Zhejiang University. His scientific interests include application of computer graphics and image, GIS. He has published more than 60 academic articles. Four research projects he has taken part in have been awarded second prize of Zhejiang Provincial Scientific and Technological Achievement. Two teaching research programs he has presided over have been awarded first prize and second prize of Zhejiang Provincial Teaching Achievement respectively.

Dzmitry Hancharou. Born in 1988. Senior Software Engineer of Flo Company. Graduated from Belarusian State University with a Master’s degree in Computer Science in 2011. Got his PhD in the field of image processing from Belarusian State University in 2016. His scientific interests include medical image recognition, deep learning and software architecture of complex systems.

Huafeng Chen. Born in 1982. Associate Professor of Zhejiang Shuren University. Graduated from Zhejiang University in 2003. In 2009 he got his PhD in the field of Earth Exploration and Information Technology at the Institute of Space Information and Technique, Zhejiang University. His scientific interests include remote sensing image processing, GIS application, image and video processing, multi-agent system. He has published more than 10 academic articles.

Alexander Nedzvedz. Born in 1970. Professor and Head of department of Belarusian State University. Graduated from Belarusian State University in 1992. Got his PhD in 2000, D.Sc. in 2013, from Belarussian Academy of Sciences. He is member of Belarusian Association for Image Analysis, member of the Scientific Council of the UIIP NAN of Belarus, member of the Scientific Council of Belarusian Republican Foundation for Fundamental Research and Recognition. For his activity he was awarded by Scholarships of the President of the Republic of Belarus in 2015. His scientific interests include computer vision, machine learning and signal processing.

Hexin Lv. Born in 1964. Professor, Full-time Deputy Director of the Academic Committee, Director of Software R&D Center, and Head of “13th Five-Year Plan” Zhejiang Provincial First-Class Discipline (B) of Computer Science and Technology of Zhejiang Shuren University. Graduated from Hangzhou Dianzi University in 1986, majoring in Computer Software. His scientific interests include artificial intelligence, computer applications, and intelligent in-formation systems.

Sergey Ablameyko. Born in 1956, DipMath in 1978, PhD in 1984, D.Sc. in 1990, Prof in 1992. Rector (President) of Belarusian State University from 2008 to 2017. His scientific interests are: image analysis, pattern recognition, digital geometry, knowledge based systems, geographical information systems, medical imaging. He has more than 400 publications. He is in Editorial Board of Pattern Recognition Letters, Pattern Recognition and Image Analysis and many other international and national journals. He is Editor-in-Chief of two national journals. He is a senior member of IEEE, Fellow of IAPR, Fellow of Belarusian Engineering Academy, Academician of National Academy of Sciences of Belarus, Academician of the European Academy, and others. He was a First Vice-President of International Association for Pattern Recognition IAPR (2006–2008), President of Belarusian Association for Image Analysis and Recognition. He is a Deputy Chairman of Belarusian Space Committee, Chairman of BSU Academic Council of awarding of PhD and D.Sc. degrees. For his activity he was awarded by State Prize of Belarus (highest national scientific award) in 2002, Belarusian Medal of F. Skoryna, Russian Award of Friendship and many other awards.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ye, S., Hancharou, D., Chen, H. et al. Extraction of Vascular Structure in 3D Cardiac CT Images by Using Object/Background Normalization. Pattern Recognit. Image Anal. 30, 237–246 (2020). https://doi.org/10.1134/S1054661820020170

Download citation


  • computed tomography images
  • background normalization
  • vascular structures