ICTACT Journal on Image and Video Processing

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Early Diagnosis of Diabetic Retinopathy by the Detection of Microaneurysms in Fundus Images


Affiliations
1 Department of Embedded System Technologies, Anna University, India
2 Department of Electrical and Electronics Engineering, Anna University, India
     

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The detection of microaneurysms is crucial, as it is an early indicator of a complication of prolonged diabetes called Diabetic Retinopathy. In this paper, an automated approach is proposed to detect microaneurysms from retinal fundus images. Firstly, the magenta plane of the input image is extracted and a few preprocessing techniques are carried out. This is followed by the localization and the removal of the optic disk. The threshold value is determined and is optimized using Firefly algorithm. Then top hat transform is applied to detect the microaneurysms. The image quality parameters and the performance parameters were calculated and analyzed on the images of the DIARETDB1 database. The experimental results yielded a sensitivity of 99.80% before optimization and 100% after optimization.

Keywords

Diabetic Retinopathy, Microaneurysms, Magenta Plane, Fundus Images, Firefly Algorithm.
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  • G. Quellec, M. Lamard, P.M. Josselin, G. Cazuguel, B. Cochener and C. Roux, “Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs”, IEEE Transactions on Medical Imaging, Vol. 27, No. 9, pp. 1230-1241, 2008.

  • Early Treatment Diabetic Retinopathy Study Research Group, “Early Photocoagulation for Diabetic Retinopathy”, Ophthalmology, Vol. 98, No. 5, pp. 766-785, 1991.

  • M. Abramoff, M. Niemeijer, M. Suttorp-Schulten, M. Viergever, S. Russell and B. Van Ginneken, “Evaluation of a System for Automatic Detection of Diabetic Retinopathy from Color Fundus Photographs in a Large Population of Patients with Diabetes”, Diabetes Care, Vol. 31, No. 2, pp. 193-198, 2008.

  • M. Niemeijer, B. Van Ginneken, J. Staal, M.S.A. Suttorp-Schulten and M.D. Abramoff, “Automatic Detection of Red Lesions in Digital Color Fundus Photographs”, IEEE Transactions on Medical Imaging, Vol. 24, No. 5, pp. 584-592, 2005.

  • K. Ram, G.D. Joshi and J. Sivaswamy, “A Successive Clutter Rejection- based Approach for Early Detection of Diabetic Retinopathy”, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 3, pp. 664-673, 2011.

  • B. Lay, “Analyse Automatique Des Images Angiofluorographiques Au Cours De La Retinopathie

  • Diabetique”, Ph.D Dissertation, Centre of Mathematical Morphology, Paris School of Mines, 1983.

  • C.E. Baudoin, B.J. Lay and J.C. Klein, “Automatic Detection of Microaneurysms in Diabetic Fluorescein

  • Angiographies”, Journal of Epidemiology and Public Health, Vol. 32, No. 3-4, pp. 254-261, 1984.

  • T. Spencer, J.A. Olson, K.C. McHardy, P.F. Sharp and J.V. Forrester, “An Image-Processing Strategy for the Segmentation and Quantification of Microaneurysms in Fluorescein Angiograms of the Ocular Fundus”, Computer and Biomedical Research, Vol. 29, No. 4, pp. 284-302, 1996.

  • G.E. Oien and P. Osnes, “Diabetic Retinopathy: Automatic Detection of Early Symptoms from Retinal Images”, Proceedings of International Norwegian Signal Processing Symposium, pp. 135-140, 1995.

  • M. Niemeijer, B. Van Ginneken, J. Staal, M.S.A. Suttorp-Schulten and M.D. Abramoff, “Automatic Detection of Red Lesions in Digital Color Fundus Photographs”, IEEE Transactions on Medical Imaging, Vol. 24, No. 5, pp. 584-592, 2005.

  • K. Ram, G.D. Joshi and J. Sivaswamy, “A Successive Clutter Rejection-based Approach for Early Detection of Diabetic Retinopathy”, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 3, pp. 664-673, 2011.

  • C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell, Michael Abramoff and P. Soliz, “Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection”, IEEE Transactions on Medical Imaging, Vol. 29, No. 2, pp. 502-512, 2010.

  • R. Priya and P. Aruna, “Review of Automated Diagnosis of Diabetic Retinopathy using the Support Vector Machine”, International Journal of Applied Engineering Research, Vol. 1, No. 4, pp. 844-863, 2011.

  • B. Li and H.K. Li, “Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends”, Current Diabetes Reports, Vol. 13, No. 9, pp. 453-459, 2013.

  • Pascale Massin and Ali Erginay, “Diabetic Retinopathy”, Elsevier, 2000.

  • T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin and J.C. Klein, “Automatic Detection of Microaneurysms in Color Fundus Images”, Medical Image Analysis, Vol. 11, No. 6, pp. 555-566, 2007.

  • Istvan Lazar, Andras Hajdu “Retinal Microaneurysm Detection through Local Rotating Cross-Section Profile Analysis”, IEEE Transactions on Medical Imaging, Vol. 32, No. 2, pp. 400-407, 2013.

  • R.J. Winder, et al., “Algorithms for Digital Image Processing in Diabetic Retinopathy”, Computerized Medical Imaging and Graphics, Vol. 33, No. 8, pp. 608-622, 2009.

  • I. Figueiredo, S. Kumar, C. Oliveira, J. Ramos and B. Engquist, “Automated Lesion Detectors in Retinal Fundus Images”, Computers in Biology and Medicine, Vol. 66, pp. 47-65, 2015.

  • Tomi Kauppi, V. Kalesnykiene, J.K. Kamarainen, R. Voutilainen, J. Pietila, H. Kalviainen and H. Uusitalo, “Diaretdb1 Diabetic Retinopathy Database Evaluation Protocol”, Available at: http://www.it.lut.fi/project/imageret/diaretdb1/

  • M.D. Saleh and C. Eswaran, “An Automated Decision-Support System for Non-Proliferative Diabetic Retinopathy”, Computer Methods and Programs in Biomedicine, Vol. 108, No. 1, pp. 186-196, 2012.

  • L. Seoud, T. Faucon, T. Hurtut, J. Chelbi, F. Cheriet and J.M.P. Langlois, “Automatic Detection of Microaneurysms and Haemorrhages in Fundus Images using Dynamic Shape Features”, Proceedings of IEEE 11th International Symposium on Biomedical Imaging, pp. 101-104, 2014.

  • A.D. Fleming, S. Philip, K.A. Goatman, J.A. Olson and P.F. Sharp, “Automated Microaneurysm Detection using Local Contrast Normalization and Local Vessel Detection”, IEEE Transactions on Medical Imaging, Vol. 25, No. 9, pp. 1223-1232, 2006.

  • A.M. Mendonca, A. Sousa, L. Mendonca and A. Campilho, “Automatic Localization of the Optic Disc by Combining Vascular and Intensity Information”, Computerized Medical Imaging and Graphics, Vol. 37, No. 5-6, pp. 409-417, 2013.

  • Syna Sreng, Noppadol Maneerat and Kazuhiko Hamamoto, “Automated Microaneurysms Detection in Fundus Images using Image Segmentation”, Proceedings of International Conference of Digital Arts, Media and Technology, pp. 233-237, 2017.

  • Wei Zhou, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi and Wenyou Du, “Automatic Microaneurysms Detection based on Multifeature Fusion Dictionary Learning,” Computational and Mathematical Methods in Medicine, Vol. 2017, pp. 1-11, 2017.

  • M.M. Habib, R.A. Welikala, A. Hoppe, C.G. Owen, A.R. Rudnicka and S.A. Barman, “Detection of Microaneurysms in Retinal Images using an Ensemble Classifier”, Informatics in Medicine Unlocked, Vol. 9, pp. 44-57, 2017.

  • X.S. Yang, “Firefly Algorithm, Stochastic Test Functions and Design Optimisation”, International Journal of Bio-Inspired Computation, Vol. 2, No. 2, pp. 78-84, 2010.

  • Geoff Dougherty, “Medical Image Processing: Techniques and Applications”, Springer, 2011.


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  • Early Diagnosis of Diabetic Retinopathy by the Detection of Microaneurysms in Fundus Images

Abstract Views: 227  |  PDF Views: 4

Authors

Jeline Devadhas
Department of Embedded System Technologies, Anna University, India
R. Binisha
Department of Electrical and Electronics Engineering, Anna University, India

Abstract


The detection of microaneurysms is crucial, as it is an early indicator of a complication of prolonged diabetes called Diabetic Retinopathy. In this paper, an automated approach is proposed to detect microaneurysms from retinal fundus images. Firstly, the magenta plane of the input image is extracted and a few preprocessing techniques are carried out. This is followed by the localization and the removal of the optic disk. The threshold value is determined and is optimized using Firefly algorithm. Then top hat transform is applied to detect the microaneurysms. The image quality parameters and the performance parameters were calculated and analyzed on the images of the DIARETDB1 database. The experimental results yielded a sensitivity of 99.80% before optimization and 100% after optimization.

Keywords


Diabetic Retinopathy, Microaneurysms, Magenta Plane, Fundus Images, Firefly Algorithm.

References