IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
3-D retinal curvature estimation
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy
Journal of Medical Systems
Detection of neovascularization for screening of proliferative diabetic retinopathy
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Computer Methods and Programs in Biomedicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
Ensemble selection for feature-based classification of diabetic maculopathy images
Computers in Biology and Medicine
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Diabetic retinopathy is a leading cause of blindness in developed countries. Diabetic patients can prevent severe visual loss by attending regular eye examinations and receiving timely treatments. In the United States, standard protocols have been developed and refined for years to provide better screening and evaluation procedures of the fundus images. Due to the emerging number of diabetic retinopathy cases, accurate and efficient evaluations of the fundus images have become a serious burden for the ophthalmologists or care providers. While diabetic retinopathy remains too complicated to call for an automatic diagnosis system, an efficient tool to facilitate the grading process with a limited number of personnel is in great demand. The current study is to develop a sorting system with a user-friendly interface, based upon the standardized early treatment diabetic retinopathy study (ETDRS) protocol, to assist the professional graders. The raw fundus images will be screened and assigned to different graders according to their skill levels and experiences. The developed hierarchical sorting process will greatly support the graders and enhance their efficiency and throughput. The proposed hybrid intelligent system with multilevel knowledge representation is used to construct this sorting system. A preliminary case study is conducted using only the features of the spot lesion group coupled with the ETDRS standard to demonstrate its feasibility and performance. The results obtained from the case study show a promising future.