title: Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.
abstract: Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.
authors.affiliation: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom. Electronic address: Roshan.Welikala@kingston.ac.uk.
authors.forename: R A
authors.initials: RA
authors.lastname: Welikala
authors.affiliation: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom.
authors.forename: J
authors.initials: J
authors.lastname: Dehmeshki
authors.affiliation: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom.
authors.forename: A
authors.initials: A
authors.lastname: Hoppe
authors.affiliation: Medical Retina, Oxford Eye Hospital, Oxford, United Kingdom.
authors.forename: V
authors.initials: V
authors.lastname: Tah
authors.affiliation: Ophthalmology Department, St. Thomas' Hospital, London, United Kingdom.
authors.forename: S
authors.initials: S
authors.lastname: Mann
authors.affiliation: Ophthalmology Department, St. Thomas' Hospital, London, United Kingdom.
authors.forename: T H
authors.initials: TH
authors.lastname: Williamson
authors.affiliation: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom.
authors.forename: S A
authors.initials: SA
authors.lastname: Barman
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009389:Neovascularization, Pathologic; D009898:Optic Disk / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods; D012160:Retina / Q000502:physiology*; D012171:Retinal Vessels / Q000033:anatomy & histology; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Dual classification; Modified line operator; New vessels; Proliferative diabetic retinopathy; Retinal images