Screening of Glaucoma disease from retinal vessel images using semantic segmentation. (May 2021)

• Record Type: Journal Article
• Authors: Imtiaz, Rakhshanda; Khan, Tariq M.; Naqvi, Syed Saud; Arsalan, Muhammad; Nawaz, Syed Junaid
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Glaucoma -- Optic disk -- Optic cup -- Image segmentation -- Deep neural networks; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107036
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A timely diagnosis of Glaucoma has crucial importance in preventing blindness. As this disease exists in the immediate vicinity of the optical disk (OD), its precise localization and segmentation are critical in its accurate diagnosis. OD consists of two parts, namely: neuroretinal and optic cup (OC). In the proposed work, the problem of OD and OC segmentation is modeled as a semantic pixel-wise labeling problem, thus bridging the gap between medical image segmentation and semantic segmentation. The proposed method eliminates the need for pre- and post-processing steps. The proposed method is evaluated for the segmentation of OD and OC on Drishti and Rim-one datasets. The offered low computational and resource requirements along with the observed state-of-the-art accuracy of the proposed method support its implementation in the real-time automatic screening of the Glaucoma disease.