Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images. Issue 3 (1st February 2019)

• Record Type: Journal Article
• Authors: Ramzan, Aneeqa; Usman Akram, Muhammad; Shaukat, Arslan; Gul Khawaja, Sajid; Ullah Yasin, Ubaid; Haider Butt, Wasi
• Is Part Of: IET image processing. Volume 13:Issue 3(2019)
• Page Start: 409
• Page End: 420
• Publication Date: 2019-02-01
• Subjects: eye -- vision defects -- medical disorders -- biomedical optical imaging -- optical tomography -- diseases -- medical image processing -- image segmentation; ILM layer -- RPE breakpoints -- disc calculation -- horizontal/flat cup diameter -- average RPE break points -- retinal level -- automated glaucoma detection -- retinal layers segmentation -- optic cup‐to‐disc ratio -- optical coherence tomography images -- eye disease -- optic disc region -- cup‐to‐disc region -- excessive intraocular pressure -- glaucoma presence -- succession -- optical coherence tomography scans -- colour channels -- quality assessment step -- segment -- different quality images -- RPE segmentation -- thresholding method -- ILM regions; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5396
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Glaucoma is a blindness causing eye disease if not treated in time and caused by the increase in the cup‐to‐disc region (CDR). A novel method for extraction of the inner limiting membrane (ILM) and retinal pigment epithelium (RPE) layers from optical coherence tomography scans is proposed. A new colour channels mean based quality assessment step is applied to segment out ILM layer for different quality images. During RPE segmentation, a new 'centroid based thresholding' method is proposed to remove extended ILM regions. The method uses both the ILM layer and RPE breakpoints for a cup and disc calculation, respectively. A novel criterion for horizontal/flat cup diameter based on the average RPE break points is proposed. Based on calculated CDRs, the system classifies the subject as normal or glaucomatous. The average sensitivity, accuracy, and specificity of the proposed system are 87, 79 and 72%, respectively, on the Armed Forces Institute of Ophthalmology dataset when correlated with clinical annotations and CDR computer generated values. The proposed system has shown the higher correlated result of 92.59% sensitivity with the senior most ophthalmologists. It promotes the e‐health field at the retinal level and employed as the decision support system by the young doctors.