Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images

• Published in: Medical & biological engineering & computing Vol. 60; no. 2; pp. 421 - 437
• Main Authors: Khaing, Tin Tin, Aimmanee, Pakinee, Makhanov, Stanislav, Haneishi, Hideaki
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cameras; Cell Phone; Computer Applications; Datasets; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diabetic Retinopathy - diagnostic imaging; Field of view; Human Physiology; Humans; Image quality; Image segmentation; Imaging; Imaging techniques; Localization; Optic Disk - diagnostic imaging; Original Article; Radiology; Retina; Retinal images; Retinal Vessels - diagnostic imaging; Retinopathy; Smartphones; Vessels; Vessel-based hybrid method; Diabetic retinopathy; Mobile camera retinal image; Optic disk localization; Optic disk segmentation
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-021-02484-x

Precise detection of the optic disk (OD) is an important task in the diagnosis of diabetic retinopathy. To manage the massive diabetic population, there is a significant demand for efficient and remote retinal imaging techniques. In this regard, the use of handheld mobile cameras attached to a smartphone is a promising approach. However, smartphone retinal images are often of low quality, compared to those obtained on standard equipment. They also have a narrow field of view and an incomplete/unbalanced vessel structure. Hence, we propose a new, fully automatic hybrid method for OD localization (HLM). It is designed for and verified on mobile camera/smartphone retinal images. The HLM analyzes the vessel structure and finds the OD locations by using the exclusion method when an image has a complete vessel system, and a newly proposed line detection method, otherwise. For OD segmentation, an active contour model followed by the circle fitting approach is integrated into the HLM. The proposed method was tested on three mobile camera datasets and four datasets obtained by standard equipment. For mobile camera datasets, the HLM achieves an average accuracy of 98% for OD localization. The segmentation routine obtains an average precision of 92.64% and an average recall of 82.38%. Testing against the recent state-of-the-art methods on the standard datasets shows comparable performance. Graphical abstract The proposed framework for OD localization and segmentation designed for and verified on mobile camera retinal datasets and standard datasets. (EM - "Exclusion Method", LDM - "Line Detection Method", OD - "Optic Disk" and PPV - "Positive Predictive Value")