Automated microaneurysm detection using local contrast normalization and local vessel detection

• Published in: IEEE transactions on medical imaging 2006-09, Vol.25 (9), p.1223-1232
• Main Authors: Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.
• Format: Article
• Language: English
• Subjects: Algorithms; Aneurysm - diagnosis; Artificial Intelligence; Automated detection; Blindness; contrast normalization; Diabetes; Diabetic Retinopathy - diagnosis; Digital photography; Diseases; Eyes & eyesight; Humans; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Lesions; microaneursysm; Pattern Recognition, Automated - methods; Physics; Reproducibility of Results; Retina; retinal imaging; Retinal Vessels - pathology; Retinopathy; Retinoscopy - methods; screening; Sensitivity and Specificity
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.879953

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%