Detection of Lesions and Classification of Diabetic Retinopathy

Authors(2) :-Anjali. A. Kunghatkar, Dr. Prof. M. S. Panse

According to the increasing consumption of sugar materials in human life and growing trend of the machine life, the prevalence of diabetes is on the rise. It is observed all patients with this disease mostly suffer from decrease or loss their vision. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. The retinal fundus images of the patients are procured by capturing the fundus of the eye with a digital fundus camera. The abnormalities in retinal fundus images due to DR are lesions which includes Microaneurysms and Haemorrhages. The proposed method is to detect the abnormalities in retinal fundus image is based on dynamic shape features and SVM classifier. In this study the Gaussian filter is used to enhance images and separate vessels with a high brightness intensity distribution. In this study canny’s edge detector is used for detection of Lesions and features are extracted. SVM is used for classification of DR as NPDR or PDR.

Authors and Affiliations

Anjali. A. Kunghatkar
Student, M.Tech Electronics and Telecommunication, Veermata Jijabai Technological Institute, Mumbai, India
Dr. Prof. M. S. Panse
Professor Department of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India

Fundus image, Lesions, Gaussian Filter, Canny edge detector, Support Vector Machine (SVM), NPDR & PDR.

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Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 702-707
Manuscript Number : IJSRST1848253
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Anjali. A. Kunghatkar, Dr. Prof. M. S. Panse, " Detection of Lesions and Classification of Diabetic Retinopathy", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.702-707, May-June-2018.
Journal URL : https://ijsrst.com/IJSRST1848253
Citation Detection and Elimination     |      | |
  • Diabetic retinopathy database and evaluation protocol (DIARETDB0). Electronic material (Online). Available online at: http://www.it.lut.fi/project/imageret/files/diaretdb0 [referred 19.2.2010].
  • Niemeijer, Meindert, et al. "Automatic detection of red lesions in digital color fundus photographs." IEEE Transactions on medical imaging 24.5 (2005): 584-592.
  • Yun, Wong Li, et al. "Identification of different stages of diabetic retinopathy using retinal optical images." Information sciences 178.1 (2008): 106-121.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  • Mumtaz, Rafia, et al. "Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients." International Journal of Diabetes in Developing Countries vol.38, issue 1,pp.80-87, 2018.
  • Kar, Sudeshna Sil, and Santi Maity. "Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy." IEEE Transactions on Biomedical Engineering , 2017.
  • Carrera, Enrique V., Andrés González, and Ricardo Carrera. "Automated detection of diabetic retinopathy using SVM." Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on. IEEE, 2017.
  • Seoud, Lama, et al. "Red lesion detection using dynamic shape features for diabetic retinopathy screening." IEEE transactions on medical imaging vol.35, issue 4, pp. 1116-1126, 2016.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  •  Zhang, Bob, BVK Vijaya Kumar, and David Zhang. "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features." IEEE transactions on biomedical engineering ,vol.61,issue 1,pp. 491-501,2014
  • Franklin, Sundararaj Wilfred, and Samuelnadar Edward Rajan. "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images." IET Image processing vol. 8,issue10 ,pp. 601-609,2014.
  •  L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Medical Imaging, vol. 32, pp. 364–375, Feb 2013.
  • Aravind, C., M. Ponnibala, and S. Vijayachitra. "Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique." IJCA Proceedings on international conference on innovations in intelligent instrumentation, optimization and electrical sciences ICIIIOES (11). 2013.
  • You, Mingli, and Yafen Li. "Automatic classification of the diabetes retina image based on improved BP neural network." Control Conference (CCC), 2014 33rd Chinese. IEEE, 2014
  • " target="_blank"> BibTeX
    |
  • Diabetic retinopathy database and evaluation protocol (DIARETDB0). Electronic material (Online). Available online at: http://www.it.lut.fi/project/imageret/files/diaretdb0 [referred 19.2.2010].
  • Niemeijer, Meindert, et al. "Automatic detection of red lesions in digital color fundus photographs." IEEE Transactions on medical imaging 24.5 (2005): 584-592.
  • Yun, Wong Li, et al. "Identification of different stages of diabetic retinopathy using retinal optical images." Information sciences 178.1 (2008): 106-121.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  • Mumtaz, Rafia, et al. "Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients." International Journal of Diabetes in Developing Countries vol.38, issue 1,pp.80-87, 2018.
  • Kar, Sudeshna Sil, and Santi Maity. "Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy." IEEE Transactions on Biomedical Engineering , 2017.
  • Carrera, Enrique V., Andrés González, and Ricardo Carrera. "Automated detection of diabetic retinopathy using SVM." Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on. IEEE, 2017.
  • Seoud, Lama, et al. "Red lesion detection using dynamic shape features for diabetic retinopathy screening." IEEE transactions on medical imaging vol.35, issue 4, pp. 1116-1126, 2016.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  •  Zhang, Bob, BVK Vijaya Kumar, and David Zhang. "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features." IEEE transactions on biomedical engineering ,vol.61,issue 1,pp. 491-501,2014
  • Franklin, Sundararaj Wilfred, and Samuelnadar Edward Rajan. "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images." IET Image processing vol. 8,issue10 ,pp. 601-609,2014.
  •  L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Medical Imaging, vol. 32, pp. 364–375, Feb 2013.
  • Aravind, C., M. Ponnibala, and S. Vijayachitra. "Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique." IJCA Proceedings on international conference on innovations in intelligent instrumentation, optimization and electrical sciences ICIIIOES (11). 2013.
  • You, Mingli, and Yafen Li. "Automatic classification of the diabetes retina image based on improved BP neural network." Control Conference (CCC), 2014 33rd Chinese. IEEE, 2014
  • " target="_blank">RIS
    |
  • Diabetic retinopathy database and evaluation protocol (DIARETDB0). Electronic material (Online). Available online at: http://www.it.lut.fi/project/imageret/files/diaretdb0 [referred 19.2.2010].
  • Niemeijer, Meindert, et al. "Automatic detection of red lesions in digital color fundus photographs." IEEE Transactions on medical imaging 24.5 (2005): 584-592.
  • Yun, Wong Li, et al. "Identification of different stages of diabetic retinopathy using retinal optical images." Information sciences 178.1 (2008): 106-121.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  • Mumtaz, Rafia, et al. "Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients." International Journal of Diabetes in Developing Countries vol.38, issue 1,pp.80-87, 2018.
  • Kar, Sudeshna Sil, and Santi Maity. "Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy." IEEE Transactions on Biomedical Engineering , 2017.
  • Carrera, Enrique V., Andrés González, and Ricardo Carrera. "Automated detection of diabetic retinopathy using SVM." Electronics, Electrical Engineering and Computing (INTERCON), 2017 IEEE XXIV International Conference on. IEEE, 2017.
  • Seoud, Lama, et al. "Red lesion detection using dynamic shape features for diabetic retinopathy screening." IEEE transactions on medical imaging vol.35, issue 4, pp. 1116-1126, 2016.
  • Paing, May Phu, Somsak Choomchuay, and MD Rapeeporn Yodprom. "Detection of lesions and classification of diabetic retinopathy using fundus images." Biomedical Engineering International Conference (BMEiCON), 2016 9th. IEEE, 2016.
  •  Zhang, Bob, BVK Vijaya Kumar, and David Zhang. "Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features." IEEE transactions on biomedical engineering ,vol.61,issue 1,pp. 491-501,2014
  • Franklin, Sundararaj Wilfred, and Samuelnadar Edward Rajan. "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images." IET Image processing vol. 8,issue10 ,pp. 601-609,2014.
  •  L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Medical Imaging, vol. 32, pp. 364–375, Feb 2013.
  • Aravind, C., M. Ponnibala, and S. Vijayachitra. "Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique." IJCA Proceedings on international conference on innovations in intelligent instrumentation, optimization and electrical sciences ICIIIOES (11). 2013.
  • You, Mingli, and Yafen Li. "Automatic classification of the diabetes retina image based on improved BP neural network." Control Conference (CCC), 2014 33rd Chinese. IEEE, 2014
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