Identification of different stages of diabetic retinopathy using retinal optical images

Diabetes is a disease which occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. This disease affects slowly the circulatory system including that of the retina. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. In this study on different stages of diabetic retinopathy, 124 retinal photographs were analyzed. As a result, four groups were identified, viz., normal retina, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. Classification of the four eye diseases was achieved using a three-layer feedforward neural network. The features are extracted from the raw images using the image processing techniques and fed to the classifier for classification. We demonstrate a sensitivity of more than 90% for the classifier with the specificity of 100%.

[1]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[2]  Ming He,et al.  Multiple fuzzy model-based temperature predictive control for HVAC systems , 2005, Inf. Sci..

[3]  Daniel C. Douek,et al.  Expression of uveitogenic retinal antigens in human thymi: Variability among antigens and individual thymi , 2003 .

[4]  Stuart Harvey Rubin,et al.  Infodynamics: Analogical analysis of states of matter and information , 2007, Inf. Sci..

[5]  Sumeet Dua,et al.  Design and implementation of a unique blood-vessel detection algorithm towards early diagnosis of diabetic retinopathy , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[6]  R. Klein,et al.  Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. , 2001, Archives of ophthalmology.

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[8]  Chanjira Sinthanayothin,et al.  Automated screening system for diabetic retinopathy , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[9]  Michael Chester,et al.  Neural networks - a tutorial , 1993 .

[10]  Peter J. Watkins,et al.  Retinopathy , 2003, BMJ : British Medical Journal.

[11]  M. Foracchia,et al.  A new tracking system for the robust extraction of retinal vessel structure , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Samuel C. Lee,et al.  Computer classification of nonproliferative diabetic retinopathy. , 2005, Archives of ophthalmology.

[13]  Peter F. Sharp,et al.  Automated microaneurysm detection , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  Heng-Da Cheng,et al.  A novel fuzzy logic approach to mammogram contrast enhancement , 2002, Inf. Sci..

[15]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[16]  Herbert F. Jelinek,et al.  Comparison of various methods to delineate blood vessels in retinal images , 2005 .

[17]  Karl-Hans Englmeier,et al.  Early detection of diabetes retinopathy by new algorithms for automatic recognition of vascular changes. , 2004, European journal of medical research.

[18]  H. Heijmans.  Morphological image operators , 1994 .

[19]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[20]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[21]  Paul Douglas,et al.  International Conference on Information Technology : Coding and Computing , 2003 .

[22]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[23]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[24]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[25]  M. Mertz,et al.  Hybrid fuzzy image processing for situation assessment [diabetic retinopathy] , 2000, IEEE Engineering in Medicine and Biology Magazine.

[26]  K. Namuduri,et al.  Automated detection and classification of vascular abnormalities in diabetic retinopathy , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[27]  M. Cree,et al.  An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice , 2006, Clinical & experimental optometry.

[28]  Kamesh Namuduri,et al.  A Decision Support Framework for Automated Screening of Diabetic Retinopathy , 2006, Int. J. Biomed. Imaging.

[29]  M. Larsen,et al.  Automated detection of diabetic retinopathy in a fundus photographic screening population. , 2003, Investigative ophthalmology & visual science.

[30]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[31]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[32]  Xiaohui Zhang,et al.  Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[33]  A. S. Neubauer,et al.  Screening der diabetischen Retinopathie und Papillentopographie mit dem „Retinal Thickness Analyzer“ (RTA) , 2004, Der Ophthalmologe.