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Contrast normalization steps for increased sensitivity of a retinal image segmentation method

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Abstract

Retinal vessel segmentation plays a major role in the detection of many eye diseases, and it is required to implement an automated algorithm for analyzing the progress of eye diseases. A variety of automated segmentation methods have been presented but almost all studies to date showed weakness in their low sensitivity toward narrow low-contrast vessels. A new segmentation method is proposed to address the issue of low sensitivity, by including modules such as principal component analysis-based color-to-gray conversion, scale normalization factors for improved narrow vessel detection, anisotropic diffusion filtering with an adequate stopping rule, and edge pixel-based hysteresis threshold. The impact of these additional steps is assessed on publicly available databases like DRIVE and STARE. For the case of DRIVE database, the sensitivity is raised from 73 to 75%, while maintaining the accuracy of 96.5%, and found to provide evidence of improved sensitivity.

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References

  1. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012)

    Article  Google Scholar 

  2. Pakter, H.M., Ferlin, E., Fuchs, S.C., Maestri, M.K., Moraes, R.S., Nunes, G., et al.: Measuring arteriolar-to-venous ratio in retinal photography of patients with hypertension: development and application of a new semi-automated method. Am. J. Hypertens. 18, 417–421 (2005)

    Article  Google Scholar 

  3. Niemeijer, M., Stall, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study on retinal vessel segmentation methods on a new publicly available database. In: Proceedings of SPIE, vol. 5370, pp. 648–656 (2004)

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

    Article  Google Scholar 

  5. Soares, J.V.B., Roberto, J.J.G.L., Cesar, M., Jelinek, J.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 

  6. Lupas, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267–1274 (2010)

    Article  Google Scholar 

  7. Xinge, Y., Qinmu, P., Yuan, Y., Yiu-ming, C., Jiajia, L.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit. 44, 10–11 (2011)

    Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Orlando, J.I., Blaschko, M.: Learning fully-connected CRFs for blood vessel segmentation in retinal images. Med. Image Comput. Comput. Assist. Interv. (MICCAI) 17, 634–641 (2014)

    Google Scholar 

  10. Huang, Y., Chen, X., Zhang, J., Zeng, D., Zhang, D., Ding, X.: Single-trial ERPs denoising via collaborative filtering on ERPs images. Neurocomputing 149(2), 914–923 (2015)

    Article  Google Scholar 

  11. Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)

    Article  Google Scholar 

  12. Wu, H.T., Huang, J., Shi, Y.Q.: A reversible data hiding method with contrast enhancement for medical images. J. Vis. Commun. Image Represent. 31, 146–153 (2015)

    Article  Google Scholar 

  13. Xu, L., Hu, Q., Hung, E., Chen, B., Tan, X., Liao, C.: Large margin clustering on uncertain data by considering probability distribution similarity. Neurocomputing 158(22), 81–89 (2015)

    Article  Google Scholar 

  14. Yin, X., Ng, B.W.H., He, J., Zhang, Y., Abbott, D.: Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping. PLoS ONE 9(4), 1–17 (2014)

    Google Scholar 

  15. Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)

    Article  Google Scholar 

  16. 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(01), 109–118 (2016)

    Article  Google Scholar 

  17. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25, 1200–1213 (2006)

    Article  Google Scholar 

  18. Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: Proceedings of the Second International KHP. Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 1, pp. 90–97. Springer, London (1999)

  19. Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parkerc, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. J. Med. Image Anal. 11(1), 47–61 (2007)

  20. Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. Azzopardia, G., Strisciuglioa, N., Ventob, M., Petkova, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)

  23. Zhao, Y., Rada, L., Chen, K., Harding, S.P., Zheng, Y.: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015)

    Article  Google Scholar 

  24. 601-5 IRB: Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios (1995)

  25. Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30, 79–116 (1998)

    Article  Google Scholar 

  26. Fehrenbach, J., Mirebeau, J.M.: Sparse non-negative stencils for anisotropic diffusion. J. Math. Imaging Vis. 49(1), 123–147 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Khan, T.M., Khan, M.A., Kong, Y., Kittaneh, O.: Stopping criterion for linear anisotropic image diffusion: a fingerprint image enhancement case. EURASIP J. Image Video Process. 6, 1–20 (2016)

    Google Scholar 

  28. Zhang, M., Li, X., Yang, Z., Yang, Y.: A novel zero-crossing edge detection method based on multi-scale space theory. In: IEEE 10th International Conference on Signal Processing, Vol. 1, pp. 1036–1039 (2010)

  29. Singla, A., Patra, S.: A fast automatic optimal threshold selection technique for image segmentation. Signal Image Video Process. 11, 243–250 (2017)

    Article  Google Scholar 

  30. Jaafari, I.E., Ansari, M.E., Koutti, L.: Fast edge-based stereo matching approach for road applications. Signal Image Video Process. 11, 267–274 (2017)

    Article  Google Scholar 

  31. Hou, Y.: Automatic segmentation of retinal blood vessels based on improved multiscale line detection. J. Comput. Sci. Eng. 8(2), 119–128 (2014)

    Article  Google Scholar 

  32. 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(03), 1118–1128 (2015)

    Google Scholar 

  33. Palomera-Perez, M.A., Martinez-Perez, M.E., Benitez-Perez, H., Ortega-Arjona, J.L.: Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans. Inf. Technol. Biomed. 14(2), 500–506 (2010)

    Article  Google Scholar 

  34. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. Med. Image Comput. Comput. Assist. Interv. 1496, 130–137 (1998)

    Google Scholar 

  35. Bankhead, P., Scholfield, C.N., McGeown, J.G., Curtis, T.M.: Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 7(3), e32435 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Sydney, Australia

    Toufique Ahmed Soomro & Manoranjan Paul

  2. Biometric and Sensor Lab, Effat University, Jeddah, Saudi Arabia

    Mohammad A. U. Khan

  3. The University of Sydney Business School, The University of Sydney, Sydney, Australia

    Junbin Gao

  4. Department of Engineering, Macquarie University, Sydney, 2109, Australia

    Tariq M. Khan

Authors
  1. Toufique Ahmed Soomro
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  2. Mohammad A. U. Khan
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  3. Junbin Gao
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  4. Tariq M. Khan
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  5. Manoranjan Paul
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Correspondence to Toufique Ahmed Soomro.

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Soomro, T.A., Khan, M.A.U., Gao, J. et al. Contrast normalization steps for increased sensitivity of a retinal image segmentation method. SIViP 11, 1509–1517 (2017). https://doi.org/10.1007/s11760-017-1114-7

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  • DOI: https://doi.org/10.1007/s11760-017-1114-7

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