Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images.
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 smart...
| Published in: | Medical & Biological Engineering & Computing Vol. 60; no. 2; pp. 421 - 438 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Feb2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154739097&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154739097 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2022 vid: 60 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154739097 154739097 NLM34988764 10.1007/s11517-021-02484-x NLM34988764 154739097 ppf: 421 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Vessel-based hybrid optic disk segmentation applied to mobile phone camera retinal images. aug: au: Khaing, Tin Tin Aimmanee, Pakinee Makhanov, Stanislav Haneishi, Hideaki affil: Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5, Tiwanont Road, Bangkadi, 12000, Meung, Pathum Thani, Thailand sug: subj: Optic Nerve Diabetic Retinopathy Retina Algorithms Ferrans and Powers Quality of Life Index ab: 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. 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"). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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