title: Automated detection of red lesions from digital colour fundus photographs.
abstract: Earliest signs of diabetic retinopathy, the major cause of vision loss, are damage to the blood vessels and the formation of lesions in the retina. Early detection of diabetic retinopathy is essential for the prevention of blindness. In this paper we present a computer-aided system to automatically identify red lesions from retinal fundus photographs. After pre-processing, a morphological technique was used to segment red lesion candidates from the background and other retinal structures. Then a rule-based classifier was used to discriminate actual red lesions from artifacts. A novel method for blood vessel detection is also proposed to refine the detection of red lesions. For a standarised test set of 219 images, the proposed method can detect red lesions with a sensitivity of 89.7% and a specificity of 98.6% (at lesion level). The performance of the proposed method shows considerable promise for detection of red lesions as well as other types of lesions.
authors.affiliation: Department of Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, Liverpool L69 3GJ, UK. h.jaafar@liverpool.ac.uk
authors.forename: Hussain F
authors.initials: HF
authors.lastname: Jaafar
authors.forename: Asoke K
authors.initials: AK
authors.lastname: Nandi
authors.forename: Waleed
authors.initials: W
authors.lastname: Al-Nuaimy
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001331:Automation; D003124:Colorimetry / Q000379:methods*; D003196:Computer Graphics; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000473:pathology*; D003952:Diagnostic Imaging / Q000379:methods*; D001330:Electronic Data Processing; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012171:Retinal Vessels / Q000473:pathology; D012680:Sensitivity and Specificity