TY - GEN
T1 - Automated Detection of Optic Disc and Exudates in Retinal Images
AU - Siddalingaswamy, P. C.
AU - Prabhu, K. Gopalakrishna
PY - 2009
Y1 - 2009
N2 - Digital colour retinal images are used by the ophthalmologists for the detection of many eye related diseases such as Diabetic retinopathy. These images are generated in large number during the mass screening of the disease and may result in biased observation due to fatigue. Automated retinal image processing system could save workload of the ophthalmologists and also assist them to extract the normal and abnormal structures in retinal images and help in grading the severity of the disease. In this paper we present a method for automatic detection of optic disc followed by classification of hard exudates pixels in retinal image. Optic disc localization is achieved by iterative threshold method to identify initial set of candidate regions followed by connected component analysis to locate the actual optic disc. Exudates are detected using k means clustering algorithm. The algorithm is evaluated against a carefully selected database of 100 color retinal images at different stages of diabetic retinopathy. The methods achieve a sensitivity of 92% for the optic disc and 86% for the detection of exudates.
AB - Digital colour retinal images are used by the ophthalmologists for the detection of many eye related diseases such as Diabetic retinopathy. These images are generated in large number during the mass screening of the disease and may result in biased observation due to fatigue. Automated retinal image processing system could save workload of the ophthalmologists and also assist them to extract the normal and abnormal structures in retinal images and help in grading the severity of the disease. In this paper we present a method for automatic detection of optic disc followed by classification of hard exudates pixels in retinal image. Optic disc localization is achieved by iterative threshold method to identify initial set of candidate regions followed by connected component analysis to locate the actual optic disc. Exudates are detected using k means clustering algorithm. The algorithm is evaluated against a carefully selected database of 100 color retinal images at different stages of diabetic retinopathy. The methods achieve a sensitivity of 92% for the optic disc and 86% for the detection of exudates.
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U2 - 10.1007/978-3-540-92841-6_67
DO - 10.1007/978-3-540-92841-6_67
M3 - Conference contribution
AN - SCOPUS:84862565885
SN - 9783540928409
VL - 23
T3 - IFMBE Proceedings
SP - 277
EP - 279
BT - 13th International Conference on Biomedical Engineering - ICBME 2008
T2 - 13th International Conference on Biomedical Engineering, ICBME 2008
Y2 - 3 December 2008 through 6 December 2008
ER -
