Title :
Decision support for automated screening of diabetic retinopathy
Author :
Kahai, Pallavi ; Namuduri, Kamesh Rao ; Thompson, Hilary
Author_Institution :
Dept. of Electr. & Comput. Eng., Wichita State Univ., KS, USA
Abstract :
Diabetic retinopathy (DR) is the leading cause of blindness. DR results in retinal disorders that include: microaneurysms, drusens, hard exudates and intra-retinal micro-vascular abnormalities (IRMA). The early signs of DR are depicted by microaneurysms among other signs. A prompt diagnosis when the disease is at the early stage can help prevent irreversible damages to the diabetic eye. This paper presents a decision support framework for automated screening of early signs of DR and classification schemes for deducing the presence or absence of microaneurysms are developed and tested under a univariate environment. The detection rule is based on binary hypothesis testing problem which simplifies the problem to yes/no decisions. An analysis of the performance of the Bayes optimality criteria is also presented in the paper.
Keywords :
Bayes methods; decision support systems; diseases; eye; feature extraction; image classification; medical image processing; Bayes optimality criteria; automated screening; binary hypothesis testing problem; classification schemes; decision support; diabetic retinopathy; feature detection; microaneurysms; retinal disorders; Automatic testing; Blindness; Computer vision; Diabetes; Diseases; Performance analysis; Retina; Retinopathy; Supervised learning; Unsupervised learning;
Conference_Titel :
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN :
0-7803-8622-1
DOI :
10.1109/ACSSC.2004.1399433