title: Automatic detection of red lesions in digital color fundus photographs.
abstract: The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.
authors.affiliation: Image Sciences Institute-Q0S.459, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands. meindert@isi.uu.nl
authors.forename: Meindert
authors.initials: M
authors.lastname: Niemeijer
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Joes
authors.initials: J
authors.lastname: Staal
authors.forename: Maria S A
authors.initials: MS
authors.lastname: Suttorp-Schulten
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D003196:Computer Graphics; D003930:Diabetic Retinopathy / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D009887:Ophthalmoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted