title: Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.
abstract: Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, 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. Electronic address: M.Fraz@kingston.ac.uk.
authors.forename: M M
authors.initials: MM
authors.lastname: Fraz
authors.affiliation: Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom. Electronic address: J.Dehmeshki@kingston.ac.uk.
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. Electronic address: A.Hoppe@kingston.ac.uk.
authors.forename: A
authors.initials: A
authors.lastname: Hoppe
authors.affiliation: Medical Retina, Oxford Eye Hospital, Oxford, United Kingdom. Electronic address: v_tah@yahoo.com.
authors.forename: V
authors.initials: V
authors.lastname: Tah
authors.affiliation: Ophthalmology Department, St Thomas' Hospital, London, United Kingdom. Electronic address: Samantha.Mann@gstt.nhs.uk.
authors.forename: S
authors.initials: S
authors.lastname: Mann
authors.affiliation: Ophthalmology Department, St Thomas' Hospital, London, United Kingdom. Electronic address: retinasurgery.co.uk@gmail.com.
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. Electronic address: S.Barman@kingston.ac.uk.
authors.forename: S A
authors.initials: SA
authors.lastname: Barman
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000235:genetics; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Dual classification; Feature selection; Genetic algorithm; New vessels; Proliferative diabetic retinopathy; Retinal images