Retinal OCT Image Registration: Methods and Applications

被引:15
|
作者
Pan, Lingjiao [1 ]
Chen, Xinjian [2 ]
机构
[1] Jiangsu Univ Technol, Sch Elect & Informat Engn, Changzhou 215000, Peoples R China
[2] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215000, Peoples R China
基金
中国国家自然科学基金;
关键词
Retina; Image registration; Imaging; Mutual information; Three-dimensional displays; Speckle; Feature extraction; medical image registration; optical coherence tomography; retina; deep learning; OPTICAL COHERENCE TOMOGRAPHY; SPECKLE NOISE-REDUCTION; MOTION CORRECTION; SD-OCT; FUNDUS; SEGMENTATION; ANGIOGRAPHY; FIELD; NEOVASCULARIZATION; DESCRIPTOR;
D O I
10.1109/RBME.2021.3110958
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed.
引用
收藏
页码:307 / 318
页数:12
相关论文
共 50 条
  • [1] Retinal OCT Image Registration: Methods and Applications
    Pan, Lingjiao
    Chen, Xinjian
    IEEE REVIEWS IN BIOMEDICAL ENGINEERING, 2023, 16 : 307 - 318
  • [2] Contrast enhancement of retinal B-scans from OCT3/Stratus by image registration - Clinical applications
    Jorgensen, Thomas Martini
    Sander, Birgit
    OPHTHALMIC TECHNOLOGIES XVII, 2007, 6426
  • [3] Combined registration and motion correction of longitudinal retinal OCT data
    Lang, Andrew
    Carass, Aaron
    Al-Louzi, Omar
    Bhargava, Pavan
    Solomon, Sharon D.
    Calabresi, Peter A.
    Prince, Jerry L.
    MEDICAL IMAGING 2016: IMAGE PROCESSING, 2016, 9784
  • [4] MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images
    Wang, Meng
    Zhu, Weifang
    Shi, Fei
    Su, Jinzhu
    Chen, Haoyu
    Yu, Kai
    Zhou, Yi
    Peng, Yuanyuan
    Chen, Zhongyue
    Chen, Xinjian
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2022, 41 (02) : 394 - 406
  • [5] Medical image registration and its application in retinal images: a review
    Nie, Qiushi
    Zhang, Xiaoqing
    Hu, Yan
    Gong, Mingdao
    Liu, Jiang
    VISUAL COMPUTING FOR INDUSTRY BIOMEDICINE AND ART, 2024, 7 (01)
  • [6] Accurately motion-corrected Lissajous OCT with multi-type image registration
    Makita, Shuichi
    Miura, Masahiro
    Azuma, Shinnosuke
    Mino, Toshihiro
    Yamaguchi, Tatsuo
    Yasuno, Yoshiaki
    BIOMEDICAL OPTICS EXPRESS, 2021, 12 (01) : 637 - 653
  • [7] Experimental Investigation of Feature Descriptors for Retinal Image Registration
    Sabanovic, Eldar
    Matuzevicius, Dalius
    2017 5TH IEEE WORKSHOP ON ADVANCES IN INFORMATION, ELECTRONIC AND ELECTRICAL ENGINEERING (AIEEE'2017), 2017,
  • [8] Quantitative analysis of retinal OCT
    Sonka, Milan
    Abramoff, Michael D.
    MEDICAL IMAGE ANALYSIS, 2016, 33 : 165 - 169
  • [9] Performance Evaluation of Retinal OCT Fluid Segmentation, Detection, and Generalization Over Variations of Data Sources
    Ndipenoch, Nchongmaje
    Miron, Alina
    Li, Yongmin
    IEEE ACCESS, 2024, 12 : 31719 - 31735
  • [10] Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications
    Zhu, Wenyue
    Kolamunnage-Dona, Ruwanthi
    Zheng, Yalin
    Harding, Simon
    Czanner, Gabriela
    BMJ OPEN OPHTHALMOLOGY, 2020, 5 (01):