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Medical Image Registration and Its Application in Retinal Images: A Review

Qiushi Nie, Xiaoqing Zhang, Yan Hu, Mingdao Gong, Jiang Liu

TL;DR

A comprehensive review of existing medical image registration methods from traditional and deep-learning-based perspectives is provided, aiming to help audiences quickly understand the development of medical image registration.

Abstract

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research.

Medical Image Registration and Its Application in Retinal Images: A Review

TL;DR

A comprehensive review of existing medical image registration methods from traditional and deep-learning-based perspectives is provided, aiming to help audiences quickly understand the development of medical image registration.

Abstract

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research.
Paper Structure (46 sections, 5 equations, 8 figures, 4 tables)

This paper contains 46 sections, 5 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Problem Formulation
  4. Transformation Types
  5. Rigid Transformation
  6. Affine Transformation
  7. Perspective Transformation
  8. Deformable Transformation
  9. Retinal Image Modalities
  10. Color Fundus Photography
  11. Fluorescein Angiography
  12. Optical Coherence Tomography
  13. Optical Coherence Tomography Angiography
  14. Traditional Image Registration Methods
  15. Intensity-based Methods
  16. ...and 31 more sections

Figures (8)

  • Figure 1: Structure of our review
  • Figure 2: The effect of different transformations
  • Figure 3: Fundus photography examples using different imaging techniques. (a) CF from FIRE dataset FIRE. (b) FA from CF-FA dataset CF-FA. (c) OCT from OCTdataset. (d) OCTA from OCTA-500 dataset OCTA-500.
  • Figure 4: A general procedure of registration using iterative optimization
  • Figure 5: A general procedure of keypoint-based registration
  • ...and 3 more figures