Skip to main content
SpringerLink
Log in

A Method of Vessel Segmentation Based on BP Neural Network for Color Fundus Images

  • Conference paper
  • First Online:
Biometric Recognition (CCBR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9967))

Included in the following conference series:

  • 2763 Accesses

Abstract

The morphological and structural changes of retinal vessels are very important for the early diagnosis of many diseases. In view of the characteristics of retinal vessels, we present a new method for vessel segmentation based on BP neural network. This method consists of four steps: histogram equalization of green channel, morphological processing, Gaussian matched filter and Hessian matrix. The fundus vessels are segmented by BP neural network. We conduct the experiments on DRIVE and STARE database. The experiment results show that our method has good effect on the segmentation of fundus retinal vessels.x

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)

    Article  Google Scholar 

  2. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi:10.1007/BFb0056195

    Chapter  Google Scholar 

  3. Zhao, Y.Q., Wang, X.H., Wang, X.F., Shih, F.Y.: Retinal vessels segmentation based on level set and region growing. Pattern Recogn. 47(7), 2437–2446 (2014)

    Article  Google Scholar 

  4. Karthika, D., Marimuthu, A., Retinal image analysis using contourlet transform and multistructure elements morphology by reconstruction. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 54–59. IEEE (2014)

    Google Scholar 

  5. Salazar-Gonzalez, A., Kaba, D., Li, Y., Liu, X.: Segmentation of the blood vessels and optic disk in retinal images. IEEE J. Biomed. Health Inf. 18(6), 1874–1886 (2014)

    Article  Google Scholar 

  6. Soares, J.V., Leandro, J.J., Cesar Jr., R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)

    Article  Google Scholar 

  7. Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

    Article  Google Scholar 

  8. Fraz, M.M., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int. J. Comput. Assist. Radiol. Surg. 9(5), 795–811 (2014)

    Article  Google Scholar 

  9. Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang, G.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149, 708–717 (2015)

    Article  Google Scholar 

  10. Hoover, V.K.A., Goldbaum, M.: Locating blood vessels in retinal images by piece-wise threhsold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)

    Article  Google Scholar 

  11. Fathi, A., Naghsh-Nilchi, A.R.: General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images. Pattern Anal. Appl. 17(1), 69–81 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Haiying Xia & Shuaifei Deng

Authors
  1. Haiying Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuaifei Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haiying Xia .

Editor information

Editors and Affiliations

  1. Sichuan University , Chengdu, China

    Zhisheng You

  2. Tsinghua University , Beijing, China

    Jie Zhou

  3. Beihang University , Beijing, China

    Yunhong Wang

  4. Chinese Academy of Sciences , Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences , Beijing, China

    Shiguang Shan

  6. Sun Yat-sen University , Guangzhou, China

    Weishi Zheng

  7. Tsinghua University , Beijing, China

    Jianjiang Feng

  8. Sichuan University , Chengdu, China

    Qijun Zhao

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Xia, H., Deng, S. (2016). A Method of Vessel Segmentation Based on BP Neural Network for Color Fundus Images. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46654-5_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46653-8

  • Online ISBN: 978-3-319-46654-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation