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Automated Red Lesion Detection: An Overview

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Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1089))

Abstract

Incidences of diabetes are increasing worldwide. An eye complication associated with uncontrolled diabetes is diabetic retinopathy. If not treated, diabetic retinopathy can be vision threatening. The microaneurysms and dot hemorrhages are the only prominent clinically observable symptoms of DR. Their timely detection can help ophthalmologists in treating abnormalities efficiently and limit the disease severity. So detecting red lesions in early stage has become an indispensable task today. This paper gives an overview of earlier proposed algorithms and methods. It also compares these algorithms based on their performance for supporting the researchers by providing the gist of these algorithms. The standard retinal image databases are also compared and discussed.

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References

  1. Antal, B., Hajdu, A.: An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans. Biomed. Eng. 59(6), 1720–1726 (2012)

    Article  Google Scholar 

  2. Baudoin, C., Lay, B., Klein, J.: Automatic detection of microaneurysms in diabetic fluorescein angiography. Revue d’épidémiologie et de santé publique 32(3–4), 254–261 (1983)

    Google Scholar 

  3. Doe, R.: Messidor: methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology techno-vision project @ONLINE (Aug 2015). http://messidor.crihan.fr/

  4. Fleming, A., Goatman, K., Williams, G., Philip, S., Sharp, P., Olson, J.: Automated detection of blot haemorrhages as a sign of referable diabetic retinopathy. In: Proc. Medical Image Understanding and Analysis (2008)

    Google Scholar 

  5. Fleming, A., Philip, S., Goatman, K., Olson, J., Sharp, P.: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans. Med. Imaging 25(9), 1223–1232 (2006)

    Article  Google Scholar 

  6. Hipwell, J., Strachan, F., Olson, J., McHardy, K., Sharp, P., Forrester, J.: Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabet. Med. 17(8), 588–594 (2000)

    Article  Google Scholar 

  7. 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 

  8. Kande, G.B., Savithri, T.S., Subbaiah, P.V.: Automatic detection of microaneurysms and hemorrhages in digital fundus images. J. Digit. Imaging 23(4), 430–437 (2010)

    Article  Google Scholar 

  9. Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kälviäinen, H., Pietilä, J.: The diaretdb1 diabetic retinopathy database and evaluation protocol. In: BMVC, pp. 1–10 (2007)

    Google Scholar 

  10. Manjaramkar, A., Kokare, M.: A rule based expert system for microaneurysm detection in digital fundus images. In: 2016 International conference on computational techniques in information and communication technologies (ICCTICT), pp. 137–140. IEEE (2016)

    Google Scholar 

  11. Manjaramkar, A., Kokare, M.: Decision trees for microaneurysms detection in color fundus images. In: 2017 International conference on innovations in green energy and healthcare technologies (IGEHT), pp. 1–4. IEEE (2017)

    Google Scholar 

  12. Manjaramkar, A., Kokare, M.: Connected component clustering based hemorrhage detection in color fundus images. Int. J. Intell. Eng. Syst. 11(2), 143–151 (2018)

    Google Scholar 

  13. Manjaramkar, A., Kokare, M.: Statistical geometrical features for microaneurysm detection. J. Digit. Imaging 31(2), 224–234 (2018)

    Article  Google Scholar 

  14. Marino, C., Ares, E., Penedo, M., Ortega, M., Barreira, N., Gomez-Ulla, F.: Automated three stage red lesions detection in digital color fundus images. WSEAS Trans. Comput. 7(4), 207–215 (2008)

    Google Scholar 

  15. Niemeijer, M., Van Ginneken, B., Cree, M.J., Mizutani, A., Quellec, G., Sánchez, C., Zhang, B., Hornero, R., Lamard, M., Muramatsu, C., et al.: Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans. Med. Imaging 29(1), 185–195 (2010)

    Article  Google Scholar 

  16. Niemeijer, M., Van Ginneken, B., Staal, J., Suttorp-Schulten, M.S., Abràmoff, M.D.: Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imaging 24(5), 584–592 (2005)

    Article  Google Scholar 

  17. Quellec, G., Lamard, M., Josselin, P.M., Cazuguel, G., Cochener, B., Roux, C.: Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans. Med. Imaging 27(9), 1230–1241 (2008)

    Article  Google Scholar 

  18. Ram, K., Joshi, G.D., Sivaswamy, J.: A successive clutter-rejection-based approach for early detection of diabetic retinopathy. IEEE Trans. Biomed. Eng. 58(3), 664–673 (2011)

    Article  Google Scholar 

  19. Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., Langlois, J.: Red lesion detection using dynamic shape features for diabetic retinopathy screening (2015)

    Google Scholar 

  20. Spencer, T., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comput. Biomed. Res. 29(4), 284–302 (1996)

    Article  Google Scholar 

  21. Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M., Van Ginneken, B., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

    Article  Google Scholar 

  22. Tang, L., Niemeijer, M., Reinhardt, J.M., Garvin, M.K., Abràmoff, M.D.: Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans. Med. Imaging 32(2), 364–375 (2013)

    Article  Google Scholar 

  23. Tavakoli, M., Shahri, R.P., Pourreza, H., Mehdizadeh, A., Banaee, T., Toosi, M.H.B.: A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recognit. 46(10), 2740–2753 (2013)

    Article  Google Scholar 

  24. Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., Klein, J.C.: Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11(6), 555–566 (2007)

    Article  Google Scholar 

  25. Zhang, B., Wu, X., You, J., Li, Q., Karray, F.: Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recognit. 43(6), 2237–2248 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. SGGS Institute of Engineering and Technology, Nanded, 431606, India

    Arati Manjaramkar & Manesh Kokare

Authors
  1. Arati Manjaramkar
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  2. Manesh Kokare
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Corresponding author

Correspondence to Arati Manjaramkar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  3. Cloud Computing, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Manjaramkar, A., Kokare, M. (2020). Automated Red Lesion Detection: An Overview. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1089. Springer, Singapore. https://doi.org/10.1007/978-981-15-1483-8_16

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  • DOI: https://doi.org/10.1007/978-981-15-1483-8_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1482-1

  • Online ISBN: 978-981-15-1483-8

  • eBook Packages: EngineeringEngineering (R0)

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