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Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 38

Machine Learning Algorithms for Retinal Image Analysis and Glaucoma Detection

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DOI: http://dx.doi.org/10.15439/2023R25

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 17–22 ()

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Abstract. In recent years machine learning technology are widely used in modern biomedical imaging systems to recognise and classify a wide range of human disorders.The development of a retinal image analysis system requires precise segmentation.in this work we are using fully connected conditional random filed model to overcome energy minimization problem as compare to potts model which is limited for elongated retinal structure as it takes pairwise potential which in turn low priority for vessel segmentaion .in this work parameters learned automatically by structured output support vector machine and gives structured predictions we use publically available data sets DRIVE,STARE,HRF and CHASEDB1 to train our system, after segmentation we are classifify them with the help of suport vector machine and K-nearest neghbour machine learning algorithms to get accurate results.we compare and validate our result with respect to sensitivity,specifity,precision,time complexity and F1 score performance metrics.

References

  1. Sixty-Sixth World Health Assembly “Towards universal eye health: a global action plan 2014–2019” of WHO https://www.emro.who.int/.
  2. R.George,Ramesh S.ve,Lingam Vijaya “Glaucoma in India:estimetaed burden of disease” in Journal of Glaucoma august 2010 volume 19 issue 6 p 391-397.
  3. C.F.Etienne “Reducing avoidable blindness and visual impairment in the region of the America”in Pan American Journal of Public Health,37(1):1-3,2015.
  4. E.Prokofyeva and Eberhartz zerenner “Epidemology of major eye diseases leading to blindness in Europe: A literature review” in Opthalmic Research,47(4):171-188,2012.
  5. Jannet Leasher,Rupert Bourne,Seth R flaxman and Jos B Jonas”Global estimates on the number of people blind or visually impaired by diabetic retinopathy: A meta-analysis from 1990-2010” in Diabetes care 39(9):1643-1649.
  6. Y.C.Than et al. “Global prevalence of glaucoma and projection of glaucoma burden through 2040: a systematic review and meta-analysis” in Ophthalmology, 121 (11):2081-2090, 2014.
  7. M.D.Abramoff,Mona K Garvin and Milan Sonka“Retinal imaging and image analysis” in IEEE Reviews Biomedical Engineering, 3:169-208, 2010.
  8. M.D.Abramoff and Nemijer”Mass screening of Diabetic retinopathy using automated methods” in Springer pp 41-50 September, 2015.
  9. M. Fraz et al.“Blood vessel segmentation methodologies in retinal images a survey,” in Computer. Methods and Programs Biomedicine., vol. 108, no. 1, pp. 407–433, 2012.
  10. A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag., vol. 19, no. 3, pp. 203–210, Mar. 2000.
  11. R. Rangayyan, F. Oloumi, F. Oloumi, P. Eshghzadeh-Zanjani, and F. Ayres, “Detection of blood vessels in the retina using gabor filters,” in Proc. Canadian Conf. Electr. Comput. Eng., 2007, pp. 717–720.
  12. A. Mendonca, and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” in IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.
  13. Benson S.Y. Lam, Yongasheng Gao and Alan Wee-Chung Liew “General retinal vessel segmentation using regularization-based multiconcavity modeling,” IEEE Trans. Med. Imag., vol. 29, no. 7, pp. 1369–1381, Jul. 2010.
  14. B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” in IEEE Trans. Med. Imag., vol. 28, no. 9, pp. 1488–1497, Sep. 2009.
  15. A. Budai, G. Michelson, and J. Hornegger, “Multiscale blood vessel segmentation in retinal fundus images,” in Proc. Bildverarbeitung fr die Med., pp. 261–265, Mar. 2010
  16. A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, “Robust vessel segmentation in fundus images,” in Int. J. Biomed. Imag., article 154860, 2013.
  17. M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Proc. SPIE,vol. 5370, pp. 648–656, 2004.
  18. J. Staal, M. Abramoff, M. Niemeijer, M. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” in IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501–509, Apr. 2004.
  19. J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification” in IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1214–1222, 2006.
  20. E. Ricci, and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” in IEEE Trans. Med. Image., vol. 26, no. 10, pp. 1357–1365, Oct. 2007.
  21. Xuming He, Richard Zemel and M.A Carriera perpinan “Multiscale conditional random fields for image labeling,” in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 2. IEEE, 2004, pp. II–695.
  22. S. Kumar and M. Hebert, “Discriminative random fields,” in Int. J. Comput. Vision, vol. 68, no. 2, pp. 179–201, Jun. 2006.
  23. S. Z. Li, “Markov Random Field Modeling in Image Analysis” in 3rd ed. Springer, 2009.
  24. J. I. Orlando and M. Blaschko, “Learning fully-connected CRFs for blood vessel segmentation in retinal images,” in MICCAI 2014.
  25. P. Kr¨ahenb¨uhl and V. Koltun. “Efficient inference in fully connected CRFs with Gaussian edge potentials” in Advances in Neural Information Processing Systems, pp. 109–117. 2012.
  26. S. Kumar and M. Hebert. “Discriminative random fields” in International Journal of Computer Vision, 68(2):179–201, 2006. ISSN 0920-5691.
  27. J.Lafferty,A Macculam and F.Preira”Conditional random fields: Probabilistic models for segmenting and labelling sequence data”in Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann Publishers Inc., 2001.
  28. Y. Boykov and V. Kolmogorov “An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision”in IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1124–1137, 2004.
  29. N. Komodakis,Nikos Paragois and Gergios Tizirtas”MRF energy minimization and beyond via dual decomposition” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):531–552, 2011.
  30. Philiip Krähenbühl and Vladlen Koltun, “Efficient inference in fully connected ¨ CRFs with Gaussian edge potentials,” in Advances in Neural Information Processing Systems, 2012, pp. 109–117.
  31. Jose Ignacio Orlando*, Elena Prokofyeva, and Matthew B. Blaschko” A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images”in IEEE Transactions On Biomedical Engineering, Vol. X, No. X, Month 2015.
  32. U. T. Nguyen,Alauddin B.,Laurence A.F Park and Kotagiri R”An effective retinal blood vessel segmentation method using multi-scale line detection” in Pattern Recognition, 46(3):703–715, 2013.
  33. J. V. Soares,J.J.G Leandro,R.M.Cesar,H.F.Jelinek and M.J Cree “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification” in IEEE Transactions on Medical Imaging, 25(9), 2006.
  34. F. Zana and J.-C. Klein.“Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation” in IEEE Transactions on Image Processing, 10(7):1010–1019, 2001.
  35. J. I. Orlando and M. del Fresno “Reviewing preprocessing and feature extraction techniques for retinal blood vessel segmentation in fundus images” in Mec´anica Computacional, XXXIII (42):2729–2743, 2014.
  36. G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov”Trainable COSFIRE filters for vessel delineation with application to retinal images” in Medical Image Analysis, 19(1):46–57, 2015.
  37. D. Mar´ın, A. Aquino, M. E. Geg´undez-Arias, and J. M. Bravo”A new supervised method for blood vessel segmentation in retinal images by using graylevel and moment invariants-based features”in IEEE Transactions on Medical Imaging, 30(1):146–158, 2011.
  38. M. M. Fraz et al., “An ensemble classification-based approach applied to retinal blood vessel segmentation,” Biomedical Engineering, IEEE Transactions on, vol. 59, no. 9, pp. 2538–2548, 2012
  39. J. Odstrcilik et al., “Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database” in IET Image Processing, vol. 7, no. 4, pp. 373–383, 2013.
  40. J. Odstrcilik,J.Jan,J.Gazárek and R.Kolář “Improvement of vessel segmentation by matched filtering in colour retinal images,” in World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany. Springer, 2009, pp. 327–330.