Vascular Tree Segmentation in Fundus Images Using Curvelet Transform

  • Rupu Kumari
  • Charul Bhatnagar
  • Anand Singh Jalal
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


In retinal images, vessel segmentation methods are an important component of circulatory blood vessel analysis systems. This paper introduces an effective approach to segment the vessels in the fundus images. The fundus images are first enhanced using curvelet transform, then segmentation is performed using morphological operations with a modified structuring element and length filtering. The proposed method has been tested on 40 images of the DRIVE database. The results demonstrate that the proposed algorithm segments blood vessels in the retinal images effectively with an accuracy of 94.33%.


Retinal Image Retinal Vessel Fundus Image Noise Standard Deviation Hypertensive Retinopathy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Youssif, A., Ghalwash, A., Ghoneim, A.: Optic Disc Detection from Normalized Digital Fundus Images by Means of a Vessels’ Direction Matched Filter. IEEE Trans. on Medical Imaging 27(1) (2008)Google Scholar
  2. 2.
    Michal, S., Charles, V.S.: Retinal Vessel Extraction using Multiscale Matched Filters, Confidence and Edge Measures. IEEE Trans. on Medical Imaging 25(12), 1531–1546 (2006)CrossRefGoogle Scholar
  3. 3.
    Candès, E., Demanet, L.: Curvelets and Fourier Integral Operators. C. R. Math. Acad. Sci. 336(5), 395–398 (2003)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast Discrete Curvelet Transforms. Society for Industrial & Applied Mathematics 5(3), 861–899 (2006)MathSciNetMATHGoogle Scholar
  5. 5.
    Jianwei, M., Plonka, G.: The Curvelet Transform. IEEE Signal Processing Magazine 27, 118–133 (2010)CrossRefGoogle Scholar
  6. 6.
    Starck, J., Murtagh, F., Candes, E., Donoho, D.: Gray and Color Image contrast Enhancement by the Curvelet Transform. IEEE Trans. Image Processing 12(6) (2003)Google Scholar
  7. 7.
    Zhen, Z., Jin-Sha, Y., Qiang, G., Ying-Hui, K.: Wavelet Image De-noising Method Based on Noise Standard Deviation Estimation. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Beijing (2007)Google Scholar
  8. 8.
    Otsu, N.: A Threshold Selection method from Gray level Histograms. IEEE Trans. Syst., Man, Cybern. S2A-9(1), 62–66 (1979)MathSciNetGoogle Scholar
  9. 9.
    Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn., pp. 627–679. Prentice-Hall, NJ (2008)Google Scholar
  10. 10.
    Niemeijer, M., Staal, J., Ginneken, B., Loog, M., Abràmoff, M.D.: Comparative Study of Retinal Vessel Segmentation Methods On a New Publicly Available Database. In: Proc. SPIE—Med. Image., vol. 5370, pp. 648–656 (2004)Google Scholar
  11. 11.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  12. 12.
    Turaga, D., Yingwei, C., Caviedes, J.: No Reference PSNR Estimation for Compressed Pictures. In: Proceedings International Conference on Image Processing, vol. 6 (2002)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Rupu Kumari
    • 1
  • Charul Bhatnagar
    • 2
  • Anand Singh Jalal
    • 2
  1. 1.Banasthali UniversityJaipurIndia
  2. 2.GLA UniversityMathuraIndia

Personalised recommendations