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A Critical Review of Expert Systems for Detection and Diagnosis of Diabetic Retinopathy

Manjiri B. Patwari, Dr. Ramesh R. Manza, Dr. Manoj Saswade, Dr. Neha Deshpande


Diabetic Retinopathy is considered as a root cause of vision loss for diabetic patients. Due to this health threat, lots of research work has been carried out on retinal images using computer science to assists medical professionals. Ten papers which use different techniques for diagnosis and detection of DR are reviewed here. Various methods such as high gray level variation, area threshold, Hough transform, back tracking technique, morphological filtering techniques, watershed transformation, principal component analysis and point distribution model have been reported for the detection and extraction of Optic Disk(OD). Various methods such as shade correction, contrast enhancement, sharpening, combination of local and global thresholding, color normalization, fuzzy C-means clustering and neural networks has been reported for the detection and classification of exudates.



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Wikipedia, the free encyclopedia

Wikipedia, the free encyclopedia

Wikipedia, the free encyclopedia

Wikipedia, the free encyclopedia

Ahmed Wasif Reza & C. Eswaran & Kaharudin Dimyati “Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation” Received: 9 September 2009 / Accepted: 27 December 2009 Springer Science+Business Media, LLC 2010

S. Kavitha & K. Duraiswamy “Automatic Detection of Hard and Soft Exudates in Fundus Images Using Color Histogram Thresholding” European Journal of Scientific Research SSN 1450-216X Vol.48 No.3 (2011), pp.493-504

V.Vijayakumari, N. Suriyanarayanan “Exudates Detection Methods in Retinal Images Using Image Processing Techniques ” International Journal of Scientific & Engineering Research, Volume 1, Issue 2, November-2010 1 ISSN 2229-5518

Arturo Aquino, Manuel Emilio Geg´undez, Diego Mar´ın “Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of Macular Edema” International Journal of Biological and Life Sciences 8:2 2012

Rangaraj M. Rangayyan,1 Xiaolu Zhu,1 Fábio J. Ayres,1 and Anna L. Ells2 “Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis” Journal of Digital Imaging, Vol 23, No 4 (August), 2010: pp 438Y453

Neera Singh & Ramesh Chandra Tripathi “Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques” international Journal of Computer Applications (0975 – 8887) Volume 8– No.2, October 2010 18

Ms. P.N. Jebarani Sargunar & Dr.R.Sukanesh, “Exudates Detection and Classification in Diabetic Retinopathy Images by Texture Segmentation Methods” International Journal of Recent Trends in Engineering, Vol 2, No. 4, November 2009,148

Ahmed Wasif Reza & C. Eswaran & Subhas Hati “Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images” Springer Science + Business Media, LLC 2007

Jagadish Nayak & P Subbanna Bhat , Rajendra Acharya U, C. M. Lim & Manjunath Kagathi “Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images” Springer Science + Business Media, LLC 2007

Katia Estabridis & Rui J.P. de figueiredo “ Automatic Detection and Diagnosis of Diabetic Retinopathy” 1-4244-1437-7/07/$20.00 2007IEEE


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