Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: A review

被引:38
|
作者
Saha, Sajib Kumar [1 ]
Xiao, Di [1 ]
Bhuiyan, Alauddin [2 ]
Wong, Tien Y. [3 ]
Kanagasingam, Yogesan [1 ]
机构
[1] CSIRO, Australian E Hlth Res Ctr, Perth, WA, Australia
[2] NYU, Sch Med, New York, NY USA
[3] Natl Univ Singapore, Singapore, Singapore
关键词
Diabetic retinopathy; Registration; Longitudinal registration; Retinopathy progression; Color fundus image; INFORMATION-BASED REGISTRATION; COMPUTER-AIDED DIAGNOSIS; RETINAL IMAGES; MICROANEURYSM TURNOVER; TEMPORAL REGISTRATION; VESSEL SEGMENTATION; MACULAR EDEMA; ALGORITHM; PHOTOGRAPHS; STEREO;
D O I
10.1016/j.bspc.2018.08.034
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Diabetic retinopathy (DR) is one of the leading cause of visual impairments in the working age population in the developed world. It is a complication of both types of diabetes mellitus, which affects the light perception part of the retina; and without timely treatment patients could lose their sight and eventually become blind. Automated methods for the detection and progression analysis of DR are considered as potential health-care need to stop disease propagation and to ensure improved management for DR. Aiming for the detection and progression analysis of DR, color fundus photography is considered as one of the best candidates for non-invasive imaging of the eye fundus. A list of methods has already been developed to analyse DR related changes in the retina using color fundus photographs. In this manuscript we review those automated methods. In order to accurately compare the evolution of DR over time, retinal images that are typically collected on an annual or biennial basis must be perfectly superimposed. However, in reality, for two separate photographic-eye examinations the patient is never in exactly the same position and also the camera may vary. Therefore, a registration method is applied prior to evolution computation. Knowing registration as a fundamental preprocessing step for longitudinal (over time) analysis, we also reviewed state-of-the art methods for the registration of color fundus images. The review summarizes the achievement so far and also identifies potential study areas for further improvement and future research toward more efficient and accurate DR progression analysis. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:288 / 302
页数:15
相关论文
共 50 条
  • [21] Automatic grading diabetic retinopathy in color fundus image: Cascaded hybrid attention network
    Liu, Yanxia
    JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES, 2024, 17 (04)
  • [22] Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine
    Sim, Dawn A.
    Keane, Pearse A.
    Tufail, Adnan
    Egan, Catherine A.
    Aiello, Lloyd Paul
    Silva, Paolo S.
    CURRENT DIABETES REPORTS, 2015, 15 (03)
  • [23] Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy
    Mudie, Lucy I.
    Wang, Xueyang
    Friedman, David S.
    Brady, Christopher J.
    CURRENT DIABETES REPORTS, 2017, 17 (11)
  • [24] Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine
    Dawn A. Sim
    Pearse A. Keane
    Adnan Tufail
    Catherine A. Egan
    Lloyd Paul Aiello
    Paolo S. Silva
    Current Diabetes Reports, 2015, 15
  • [25] Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy
    Lucy I. Mudie
    Xueyang Wang
    David S. Friedman
    Christopher J. Brady
    Current Diabetes Reports, 2017, 17
  • [26] Detecting diabetic retinopathy by automated image analysis.
    Hansen, AB
    Hartvig, NV
    Jensen, MS
    Larsen, M
    Lund-Andersen, H
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2004, 45 : U365 - U365
  • [27] Automated Detection and Classification of Bright Lesions Associated with Diabetic Retinopathy from Color Fundus Images
    Gao, Wei-wei
    Shen, Jian-xin
    Wang, Yu-liang
    Liang, Chun
    INTERNATIONAL CONFERENCE ON BIOLOGICAL, MEDICAL AND CHEMICAL ENGINEERING (BMCE 2013), 2013, : 16 - 20
  • [28] Automated detection of diabetic retinopathy on digital fundus images
    Sinthanayothin, C
    Boyce, JF
    Williamson, TH
    Cook, HL
    Mensah, E
    Lal, S
    Usher, D
    DIABETIC MEDICINE, 2002, 19 (02) : 105 - 112
  • [29] A review on automatic analysis techniques for color fundus photographs
    Besenczi, Renato
    Toth, Janos
    Hajdu, Andras
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2016, 14 : 371 - 384
  • [30] Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image
    Ali, Aqib
    Qadri, Salman
    Mashwani, Wali Khan
    Kumam, Wiyada
    Kumam, Poom
    Naeem, Samreen
    Goktas, Atila
    Jamal, Farrukh
    Chesneau, Christophe
    Anam, Sania
    Sulaiman, Muhammad
    ENTROPY, 2020, 22 (05)