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Classification and Detection of Diabetic Retinopathy

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Advances in Intelligent Analysis of Medical Data and Decision Support Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 473))

Abstract

Diabetic retinopathy (DR) is the leading cause of blindness in adults around the world today. Early detection (that is, screening) and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. In this paper, the four retinal abnormalities (microaneurysms, haemorrhages, exudates, and cotton wool spots) are located in 100 color retinal images, previously graded by an ophthalmologist. A new automatic algorithm has been developed and applied to 100 retinal images. Accuracy assessment of the classified output revealed the detection rate of the microaneurysms was 87% using the thresholding method, whereas the detection rate for the haemorrhages was 88%. On the other hand, the correct classification rate for microaneurysms and haemorrhages using the minimum distance classifier was 60% and 94% respectively. The thresholding method resulted in a correct detection rate for exudates and cotton wool spots of 93% and 89% respectively. The minimum distance classifier gave a correct rate for exudates and cotton wool spots of 95% and 86% respectively.

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References

  1. Morello, C.: Etiology and Natural History of Diabetic Retinopathy: an Overview. Am. J. Health Syst. Pharm. 64(17 suppl. 12), 3–7 (2007)

    Article  Google Scholar 

  2. Gardner, T.W., Antonetti, D.A., Barber, A.J., LaNoue, K.F., Levison, S.W.: Diabetic Retinopathy: More than Meets the Eye. Surv. Ophthalmol. 47(suppl. 2), S253–S262 (2002)

    Article  Google Scholar 

  3. Serrarbassa, P.D., Dias, A.F., Vieira, M.F.: New Concepts on Diabetic Retinopathy: Neural Versus Vascular Damage. Arq. Bras. Oftalmol. 71(3), 459–463 (2008)

    Article  Google Scholar 

  4. Barber, A.J.: A New View of Diabetic Retinopathy: a Neurodegenerative Disease of the Eye. Prog. Neuropsychopharmacol. Biol. Psychiatry 27(2), 283–290 (2003)

    Article  MathSciNet  Google Scholar 

  5. Goatman, K., Charnley, A., Webster, L., Nussey, S.: Assessment of Automated Disease Detection in Diabetic Retinopathy Screening Using Two-field Photography. PLoS One 6(12), e27524 (2011)

    Google Scholar 

  6. Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and Classification of Diabetic Retinopathy Using Retinal Images. In: Annual IEEE India Conf (INDICON), pp. 1–6 (2011)

    Google Scholar 

  7. Jones, S., Edwards, R.T.: Diabetic Retinopathy Screening: a Systematic Review of the Economic Evidence. Diabet. Med. 27(3), 249–256 (2010)

    Article  Google Scholar 

  8. Rodgers, M., Hodges, R., Hawkins, J., Hollingworth, W., Duffy, S., McKibbin, M., Mansfield, M., Harbord, R., Sterne, J., Glasziou, P., Whiting, P., Westwood, M.: Colour Vision Testing for Diabetic Retinopathy: a Systematic Review of Diagnostic Accuracy and Economic Evaluation. Health Technol. Assess. 13(60), 1–160 (2009)

    Google Scholar 

  9. Farley, T.F., Mandava, N., Prall, F.R., Carsky, C.: Accuracy of Primary Care Clinicians in Screening for Diabetic Retinopathy Using Single-image Retinal Photography. Ann. Fam. Med. 6(5), 428–434 (2008)

    Article  Google Scholar 

  10. Bloomgarden, Z.T.: Screening for and Managing Diabetic Retinopathy: Current Approaches. Am. J. Health Syst. Pharm. 64(17 suppl. 12), S8–S14 (2007)

    Article  Google Scholar 

  11. Chew, E.Y.: Screening Options for Diabetic Retinopathy. Curr. Opin. Ophthalmol. 17(6), 519–522 (2006)

    Article  MathSciNet  Google Scholar 

  12. Sinclair, S.H.: Diabetic Retinopathy: the Unmet Needs for Screening and a Review of Potential Solutions. Expert. Rev. Med. Devices 3(3), 301–313 (2006)

    Article  MathSciNet  Google Scholar 

  13. Xu, J., Hu, G., Huang, T., Huang, H., Chen, B.: Using Multifocal ERG Responses to Discriminate Diabetic Retinopathy. Doc Ophthalmol. 112(3), 201–207 (2006)

    Article  Google Scholar 

  14. Jin, X., Guangshu, H., Tianna, H., Houbin, H., Bin, C.: The Multifocal ERG in Early Detection of Diabetic Retinopathy. Conf.Proc. IEEE Eng. Med. Biol. Soc. 7, 7762–7765 (2005)

    Google Scholar 

  15. Dupas, B., Walter, T., Erginay, A., Ordonez, R., Deb-Joardar, N., Gain, P., Klein, J.C., Massin, P.: Evaluation of Automated Fundus Photograph Analysis Algorithms for Detecting Microaneurysms, Haemorrhages and Exudates, and of a Computer-assisted Diagnostic System for Grading Diabetic Retinopathy. Diabetes Metab. 36(3), 213–220 (2010)

    Article  Google Scholar 

  16. Fleming, A.D., Goatman, K.A., Philip, S., Williams, G.J., Prescott, G.J., Scotland, G.S., McNamee, P., Leese, G.P., Wykes, W.N., Sharp, P.F., Olson, J.A.: The Role of Haemorrhage and Exudate Detection in Automated Grading of Diabetic Retinopathy. Br. J. Ophthalmol. 94(6), 706–711 (2010)

    Article  Google Scholar 

  17. Fleming, A.D., Philip, S., Goatman, K.A., Williams, G.J., Olson, J.A., Sharp, P.F.: Automated Detection of Exudates for Diabetic Retinopathy Screening. Phys. Med. Biol. 52(24), 7385–7396 (2007)

    Article  Google Scholar 

  18. Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated Microaneurysm Detection Using Local Contrast Normalization and Local Vessel Detection. IEEE Trans. Med. Imaging 25(9), 1223–1232 (2006)

    Article  Google Scholar 

  19. Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal Image Analysis: Concepts, Applications and Potential. Prog. Retin. Eye Res. 25(1), 99–127 (2006)

    Article  Google Scholar 

  20. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)

    Article  Google Scholar 

  21. Marrugo, A.G., Millan, M.S.: Retinal Image Analysis: Preprocessing and Feature Extraction. Journal of Physics: Conference Series 274, 012039 (2011), doi:10.1088/1742-6596/274/1/012039

    Article  Google Scholar 

  22. 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 

  23. Kavitha, G., Ramakrishnan, S.: Abnormality Detection in Retinal Images Using ant Colony Optimization and Artificial Neural Networks - Biomed 2010. Biomed. Sci. Instrum. 46, 331–336 (2010)

    Google Scholar 

  24. Sri Madhava, N.R., Kavitha, G., Ramakrishnan, S.: Assessment of Retinal Vasculature Abnormalities Using Slantlet Transform Based Digital Image Processing - Biomed 2011. Biomed. Sci. Instrum. 47, 88–93 (2011)

    Google Scholar 

  25. Anitha, J., Selvathi, D., Hemanth, D.J.: Neural Computing Based Abnormality Detection in Retinal Optical Images. In: Proc. of the IEEE International Advance Computing Conf., Patiala, pp. 630–635 (2009), doi:10.1109/IADCC.2009.4809085

    Google Scholar 

  26. Anitha, J., Vijila, C.K., Hemanth, D.J., Ahsina, A.: Self Organizing Neural Network Based Pathology Classification in Retinal Images. In: Proc. of the World Congress on Nature and Biologically Inspired Computing, Coimbatore, pp. 1457–1462 (2009), doi:10.1109/NABIC.2009.5393697

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Misr University for Science & Technology  (MUST), 6th Of October City, Cairo, Egypt

    Ahmad Taher Azar

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

Authors
  1. Ahmad Taher Azar
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  2. Valentina E. Balas
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Correspondence to Ahmad Taher Azar .

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Editors and Affiliations

  1. Technical University of Sofia, Boul. Kl. Ohridsky 8, Sofia, 1000, Bulgaria

    Roumen Kountchev

  2. Petru Maior University, Str. Nicolae Iorga 1, Targu Mures, 540 088, Romania

    Barna Iantovics

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Azar, A.T., Balas, V.E. (2013). Classification and Detection of Diabetic Retinopathy. In: Kountchev, R., Iantovics, B. (eds) Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Studies in Computational Intelligence, vol 473. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00029-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-00029-9_12

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00028-2

  • Online ISBN: 978-3-319-00029-9

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