Advertisement

GGM classifier with multi-scale line detectors for retinal vessel segmentation

  • Mohammad A. U. Khan
  • Tariq M. KhanEmail author
  • Syed S. Naqvi
  • M. Aurangzeb Khan
Original Paper
  • 10 Downloads

Abstract

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation.

Keywords

Image segmentation Vessel segmentation Retinal images Diabetic retinopathy 

Notes

Acknowledgements

The authors would like to thank Effat University in Jeddah, Saudi Arabia, for funding the research reported in this paper through the Research and Consultancy Institute.

Supplementary material

11760_2019_1515_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (docx 20 KB)

References

  1. 1.
    Knudtson, M.D., Klein, B.E.K., Klein, R., Wong, T.Y., Hubbard, L.D.: Variation associated with measurement of retinal vessel diameters at different points in the pulse cycle. J. Ophthalmol. 88, 57–61 (2004)Google Scholar
  2. 2.
    Fischer, J.G., Mewes, H., Hopp, H.H., Schubert, R.: Analysis of pressurized resistance vessel diameter changes with a low cost digital image processing device. Comput. Methods Prog. Biomed. 50, 23–030 (1996)CrossRefGoogle Scholar
  3. 3.
    Tyml, K., Anderson, D., Lidington, D., Ladak, H.M.: A new method for assessing arteriolar diameter and hemodynamic resistance using image analysis of vessel lumen. Am. J. Physiol. Heart Circ. Physiol. 284, H1721–8 (2003)CrossRefGoogle Scholar
  4. 4.
    Patton, N., Aslam, T., Macgillivray, T., Pattie, A., Deary, I.J.: Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J. Anat. 206, 319–348 (2005)CrossRefGoogle Scholar
  5. 5.
    Wang, J.J., Liew, G., Klein, R., Rochtchina, E., Knudtson, M.D.: Retinal vessel diameter and cardiovascular mortality: pooled data analysis from two older populations. Eur. Heart J. 28, 1984–1992 (2007)CrossRefGoogle Scholar
  6. 6.
    Soomro, T.A., Khan, T.M., Khan, M.A.U., Gao, J., Paul, M., Zheng, L.: Impact of ica-based image enhancement technique on retinal blood vessels segmentation. IEEE Access 6, 3524–3538 (2018)CrossRefGoogle Scholar
  7. 7.
    Soomro, T.A., Gao, J., Khan, T., Hani, A.F.M., Khan, M.A.U., Paul, M.: Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey. Pattern Anal. Appl. 20(4), 927–961 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Khan, M.A.U., Khan, T.M., Bailey, D.G., Soomro, T.A.: A generalized multi-scale line-detection method to boost retinal vessel segmentation sensitivity. Pattern Anal. Appl. (2018).  https://doi.org/10.1007/s10044-018-0696-1 Google Scholar
  9. 9.
    Khan, M.A.U., Khan, T.M., Soomro, T.A., Mir, N., Gao, J.: Boosting sensitivity of a retinal vessel segmentation algorithm. Pattern Anal. Appl. 22(2), 583–599 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lan, X., Zhang, S., Yuen, P.C., Chellappa, R.: Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans. Image Process. 27(4), 2022–2037 (2018)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Lan, X., Ma, A.J., Yuen, P.C.: Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1194–1201 (2014)Google Scholar
  12. 12.
    Lan, X., Ma, A.J., Yuen, P.C., Chellappa, R.: Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans. Image Process. 24(12), 5826–5841 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Lan, X., Zhang, S., Yuen, P.C.: Robust joint discriminative feature learning for visual tracking. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. IJCAI’16, pp. 3403–3410, AAAI Press (2016)Google Scholar
  14. 14.
    Lan, X., Yuen, P.C., Chellappa, R.: Robust mil-based feature template learning for object tracking. In: AAAI (2017)Google Scholar
  15. 15.
    Lan, X., Ye, M., Zhang, S., Yuen, P.C.: Robust collaborative discriminative learning for rgb-infrared tracking. In: AAAI (2018)Google Scholar
  16. 16.
    Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)CrossRefGoogle Scholar
  17. 17.
    Nguyen, U.T.V., Bhuiyan, A., Park, L.A.F., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit. 46, 703–715 (2013)CrossRefGoogle Scholar
  18. 18.
    Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J. Biomed. Health Inform. 19(3), 1118–1128 (2015)Google Scholar
  19. 19.
    Fraz, M.M., Remagnin, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Programs Biomed. 108, 407–433 (2012)CrossRefGoogle Scholar
  20. 20.
    Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)CrossRefGoogle Scholar
  21. 21.
    Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang, G.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 708, 149–717 (2015)Google Scholar
  22. 22.
    Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)CrossRefGoogle Scholar
  23. 23.
    Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)CrossRefGoogle Scholar
  24. 24.
    Fu, H., Xu, Y., Wong, D.W.K., Liu, J.: Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 698–701 (2016)Google Scholar
  25. 25.
    Hou, Y.: Automatic segmentation of retinal blood vessels based on improved multiscale line detection. J. Comput. Sci. Eng. 8(2), 119–128 (2014)CrossRefGoogle Scholar
  26. 26.
    Niemeijer, M., Staal, J., van Ginneken, B.: Comparative study on retinal vessel segmentation methods on a new publicly available database. SPIE (2004)Google Scholar
  27. 27.
    Pridmore, T.P.: Thresholding images of line drawings with hysteresis. In: Fourth International Workshop on Graphics Recognition Algorithms and Applications, pp. 310–319 (2002)Google Scholar
  28. 28.
    Canny, A.J.: Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  29. 29.
    Kass, M., Witkin, A.: Analyzing oriented patterns. Comput. Vis. Graph. Image Process. 37(3), 362–385 (1987)CrossRefGoogle Scholar
  30. 30.
    Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2015)CrossRefGoogle Scholar
  31. 31.
    Staal, J., Abramoff, 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)CrossRefGoogle Scholar
  32. 32.
    Orlando, J.I., Blaschko, M.: Learning fully-connected crfs for blood vessel segmentation in retinal images. Med. Image Comput. Comput. Assist. Interv. 17, 634–641 (2014)Google Scholar
  33. 33.
    Soomro, T.A., Khan, M.A.U., Gao, J., Khan, T.M., Paul, M.: Contrast normalization steps for increased sensitivity of a retinal image segmentation method. Signal, Image Video Process. 11(8), 1509–1517 (2017)CrossRefGoogle Scholar
  34. 34.
    Soomro, T.A., Khan, M.A.U., Gao, J., Khan, T.M., Paul, M., Mir, N.: Automatic retinal vessel extraction algorithm. In: DICTA, pp. 1–8 (2016)Google Scholar
  35. 35.
    Khan, M.A.U., Soomro, T.A., Khan, T.M., Bailey, D.G., Gao, J., Mir, N.: Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, pp. 1–5 (2016)Google Scholar
  36. 36.
    Soares, J.V.B., Leandro, J.J.G., 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, 1214–22 (2006)CrossRefGoogle Scholar
  37. 37.
    Lupas, C.A., Tegolo, D., Trucco, E.: Fabc: retinal vessel segmentation using adaboost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267–1274 (2010)CrossRefGoogle Scholar
  38. 38.
    Azzopardia, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable cosfire filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biometric LabEffat University Research and Consultancy InstituteJeddahSaudi Arabia
  2. 2.Department of Electrical and Computer EngineeringCOMSATS University IslamabadIslamabadPakistan
  3. 3.School of Computing and Communication, Infolab21Lancaster UniversityLancasterUK

Personalised recommendations