人工智能
规范化(社会学)
计算机视觉
计算机科学
分级(工程)
糖尿病性视网膜病变
模式识别(心理学)
对比度(视觉)
视网膜
医学
眼科
工程类
土木工程
社会学
内分泌学
糖尿病
人类学
作者
Alan Fleming,Sam Philip,Keith A Goatman,John A. Olson,P. Sharp
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2006-09-01
卷期号:25 (9): 1223-1232
被引量:286
标识
DOI:10.1109/tmi.2006.879953
摘要
Screening programs using retinal photography for the detection of diabetic eye disease are being introduced in the U.K. and elsewhere. Automatic grading of the images is being considered by health boards so that the human grading task is reduced. Microaneurysms (MAs) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy. This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina. Various methods for contrast normalization are compared. Best results were obtained with a method that uses the watershed transform to derive a region that contains no vessels or other lesions. Dots within vessels are handled successfully using a local vessel detection technique. Results are presented for detection of individual MAs and for detection of images containing MAs. Images containing MAs are detected with sensitivity 85.4% and specificity 83.1%.
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