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Automatic Diagnosis of Diabetic Retinopathy Micro aneurysm from Low Contrast Retinal Images using Mathematical Morphology Methods

by Raju Maher, Sangramsing Kayte, Dnyaneshwar Panchal, Suvarnsing G. Bhable, Pankaj Sathe, Jaypalsing Kayte
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 6
Year of Publication: 2015
Authors: Raju Maher, Sangramsing Kayte, Dnyaneshwar Panchal, Suvarnsing G. Bhable, Pankaj Sathe, Jaypalsing Kayte
10.5120/ijca2015907040

Raju Maher, Sangramsing Kayte, Dnyaneshwar Panchal, Suvarnsing G. Bhable, Pankaj Sathe, Jaypalsing Kayte . Automatic Diagnosis of Diabetic Retinopathy Micro aneurysm from Low Contrast Retinal Images using Mathematical Morphology Methods. International Journal of Computer Applications. 130, 6 ( November 2015), 50-56. DOI=10.5120/ijca2015907040

@article{ 10.5120/ijca2015907040,
author = { Raju Maher, Sangramsing Kayte, Dnyaneshwar Panchal, Suvarnsing G. Bhable, Pankaj Sathe, Jaypalsing Kayte },
title = { Automatic Diagnosis of Diabetic Retinopathy Micro aneurysm from Low Contrast Retinal Images using Mathematical Morphology Methods },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 6 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 50-56 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number6/23216-2015907040/ },
doi = { 10.5120/ijca2015907040 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:24:40.536469+05:30
%A Raju Maher
%A Sangramsing Kayte
%A Dnyaneshwar Panchal
%A Suvarnsing G. Bhable
%A Pankaj Sathe
%A Jaypalsing Kayte
%T Automatic Diagnosis of Diabetic Retinopathy Micro aneurysm from Low Contrast Retinal Images using Mathematical Morphology Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 6
%P 50-56
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetic retinopathy is a disease of the retina, occurring in about a quarter of people with diabetes. The retina contains cells that convert the light into the electric signals, and these signals are then sent on to the brain. The symptoms can blur or distort the patient’s vision and are a main cause of blindness. Microaneurysm are one of the primary signs of retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a morphological method which takes time and affects patients. The leakage causes the retina to swell up a little and become waterlogged, a bit like a sponge. This swelling then damages the retinal cells themselves. This paper examines and proposes a set of optimally adjusted morphological operators to be used for Microaneurysm detection on diabetic retinopathy low-contrast images. Automatic tool for diagnosis of diabetic retinopathy must go through some well-defined steps. First, it has to detect the blood vessels and optic disc. Second, it has to identify abnormalities in the retina like exudates and microaneurysms that cause diabetic retinopathy. This method has resulted with sensitivity and specificity for our Microaneurysm detection is 80% and 99.5%, respectively.

References
  1. World Diabetes, A newsletter from the World Health Organization, 4, 1998.
  2. Hove MN, Kristensen JK, Lauritzen T, Bek T. Quantitative analysis of retinopathy in type 2 diabetes: dentification of prognostic parameters for developingvisuallosssecondarytodiabeticmaculopathy.ActaOphthalmol Scand 2004;82:379–85.
  3. Hsu W, Pallawala PMDS, Mong Li Lee, Kah-Guan Au Eong. The role of domain knowledge in the detection of retinal hard exudates. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition IEEE Computer Society, vol. 2. 2001. p. II-243–51.
  4. Kanski J. Diabetic retinopathy, clinical ophthalmology. Oxford: Butterworth-Heimann; 1997.
  5. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S. Automated detection of diabetic retinopathy on digital fundus image. J Diabet Med 2002;19:105–12.
  6. Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MS, Abramoff MD.Automaticdetectionofredlesionsindigitalcolorfundusphotographs.
  7. IEEE Trans Med Imag 2005;24:584–92.
  8. Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 2004;21:84–90.
  9. Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 1993;80:940–4.
  10. ZhengLiu,OpasC,KrishnanSM.Automaticimageanalysisoffundusphotograph. In: Proceedings of the International Conference on Engineering in Medicine and Biology, vol. 2. 1997. p. 524–5.
  11. Osareh A, Mirmehdi M, Thomas B, Markham R. Automated identification of diabetic retinal exudates in digital colour images. Br J Ophthalmol 2002;87:1220–2.
  12. Mitra SK, Te-Won Lee, Goldbaum M. Bayesian network based sequential inference for diagnosis of diseases from retinal images. Pattern Recogn Lett 2005;23:459–70.
  13. ETDRSReportNumber10,Gradingdiabeticretinopathyfromstereoscopic color fundus photographs—an extension of the modified Airlie house classification. Ophthalmology 1991;98:783–803.
  14. Davis MD, Bressler SB, Aiello LP, Bressler NM, Browning DJ, Flaxel CJ, et al. Comparison of time-domain oct and fundus photographic assessments of retinal thickening in eyes with diabetic macular edema. Invest Ophthalmol Vis Sci 2008;49:1745–52.
  15. Gonzales RC, Woods RE. Digital image processing. New York: AddisonWesley; 1992. p. 75–140.
  16. Sinthanayothin C, Boyce JF, Cook HL, Williamson TH. Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 1999;82:902–10.
  17. Walter T, Klein JC, Massin P, Erginay A. A Contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in colour fundus images of the human retina. IEEE Trans Med Imag 2002;21:1223–42.
  18. Sanchez CI, Hornero R, Lopez MI. Retinal image analysis to detect and quantify lesionsassociated withdiabeticretinopathy .In: Proceedingsofthe International Conference on Engineering in Medicine and Biology Society IEEE. 2004. p. 1324–7.
  19. Zhang X, Opas C. Detection and classification of bright lesions in colour fundus images. In: Proceedings of the International Conference on Image Processing IEEE. 2004. p. 129–42.
  20. JavittJC,AielloLP,BassiLJ,ChiangEP,CannerJK,KleinR.Detectingand treatingretinopathyinpatientswithtypeIdiabetesmellitus.Savingsassociated with improved implementation of current guidelines. Ophthalmology 1991;98:1535–72.
  21. Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., and Tamura, T., Computer based detection of diabetes retinopathy stages using digital fundus images. J. Eng. Med. 223(H5):545–553, 2009.
  22. [Raju Maher, Dr.Mukta Dhopeshwarkar “Automated Detection of Non-proliferative Diabetes Retinopathy Using Fundus Images” International Journal of Advanced Research in Computer Science and Software Engineering Volume 5, Issue 3, March 2015.
  23. Raju Sahebrao Maher, Sangramsing N. Kayte, Sandip T. Meldhe, Mukta Dhopeshwarkar, “Automated Diagnosis Non-proliferative Diabetic Retinopathy in Fundus Images using Support Vector Machine” International Journal of Computer Applications (0975 – 8887)Volume 125 – No.15, September 2015.
  24. Raju Sahebrao Maher, Dnyaneshwar S. Panchal, Sangramsing Kayte, Dr. Mukta Dhopeshwarkar” Automatic Identification of Varies Stages of Diabetic Retinopathy Using Retinal Fundus Images” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 9, September 2015.
  25. Raju Maher, Sangramsing Kayte, Dnyaneshwar Panchal, Pankaj Sathe, Sandip Meldhe, “A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy” International Journal of Emerging Research in Management and Technology, Volume-4,Issue-10, October-2015.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetic retinopathy Microaneurysm Morphology operator Retinal image Non-dilated retinal images.

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