Computational Retinal Image Analysis - 1st Edition - ISBN: 9780081028162

Computational Retinal Image Analysis

1st Edition

Tools, Applications and Perspectives

Editors: Emanuele Trucco Tom MacGillivray Yanwu Xu
Paperback ISBN: 9780081028162
Imprint: Academic Press
Published Date: 25th November 2019
Page Count: 502
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Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more.

Key Features

  • Provides a unique, well-structured and integrated overview of retinal image analysis
  • Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care
  • Includes plans and aspirations of companies and professional bodies


Researchers and graduate students in biomedical engineering, engineering, and computer science working in medical imaging, healthcare informatics, computational ophthalmologists, optomerists

Table of Contents

1. A brief history of RIA 
Manuel Trucco, Tom MacGillivray and Frank Xu
2. Clinical motivation and the needs for RIA in healthcare
Ryo Kawasaki
3. Techniques and principles for retinal imaging
Andrew Robert Harvey, Guillem Carles and Andy I. McNaught
4. Preprocessing, image enhancement and registration of retinal images
Carlos Hernandez-Matas, Antonis A. Argyros and Xenophon Zabulis
5. Locating anatomical landmarks 
Jeffrey Wigdahl, Pedro Guimaraes and Alfredo Ruggeri
6. Vasculature Analysis: Segmentation, Synthesis, Tracing, and Classification
Li Cheng, He ZHAO, Xingzheng Lyu, Huazhu Fu and HUIQI LI
7. OCT layer Segmentation
Raphael Sznitman, Stefanos Apostolopoulos, Sandro De Zanet, Carlos Ciller and Sebastian Wolf
8. Image Quality Assessment
Sarah Ann Barman, Christopher Grant Owen, Alicja Regina Rudnicka and Roshan Alex Welikala
9. Validation
Manuel Trucco and Tom MacGillivray
10. Statistical analysis and design in ophthalmology: towards optimising your data
Catey Bunce and Gabriela Czanner
11. Glaucoma and optic disc diseases
Jun Cheng, Zhengguo Li, Huazhu Fu, Zaiwang Gu, Damon Wing Kee Wong and Jiang Liu
12. Diabetic Retinopathy and Maculopathy lesions
Bashir Ibrahim Al-Diri, Piotr Lukasz Chudzik, Giovanni Ometto, Francesco Caliva and Maged Selim Habib
13. Drusen and macula degeneration lesions
Bryan Michael Williams, Philip Ian Burgess and Yalin Zheng
14. OCT scanning centered on ONH and macula
Frank Xu
15. OCT fluid detection and quantification
Hrvoje Bogunovic, Wolf-Dieter Vogl, Sebastian Waldstein and Ursula Schmidt-Erfurth
16. Fluorescein angiography image analysis
17. Retinal biomarkers and cardiovascular disease: a clinical perspective
Carol Y. Cheung, Posey Wong and Tien Yin Wong
18. Diabetic retinopathy screening
Fan Huang, Behdad Dashtbozorg, Samaneh Abbasi, Qingjiong Zhang, Erik J. Bekkers and Bart M. ter Haar Romeny
19. Image analysis tools for assessment of atrophic macular diseases
Zhihong Hu and SriniVas Sadda
20. Artificial intelligence and deep learning in retinal image analysis
Philippe Burlina, Adrián Galdrán, Pedro Costa, Adam Cohen and Aurélio Campilho
21. Big-data analysis of RIA measurements
Levan La, Xing Li, Varun Arora, Haifeng Wang, Yehui Yang, Dalu Yang, Lei Wang, Yanwu Xu and Yan Huang
22. The challenges of assembling, maintaining and making available large data sets of clinical data for research
Emily Jefferson
23. Technical and clinical challenges of AI in retinal image analysis
Wynne Hsu


No. of pages:
© Academic Press 2019
25th November 2019
Academic Press
Paperback ISBN:

About the Editor

Emanuele Trucco

Manuel has been active since 1984 in computer vision, and since 2002 in medical image analysis. He has published more than 250 refereed papers and 2 textbooks (one of which an international standard with 2,793 citations, Google Scholar 25 Oct 2016). He is co-director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (co-director Dr T MacGillivray). VAMPIRE develops software tools for efficient data and image analysis, with a focus on multi-modal retinal images. VAMPIRE has been used in UK and international biomarker studies on cardiovascular risk, stroke, dementia, diabetes and complications, cognitive performance, neurodegenerative diseases, and genetics.

Affiliations and Expertise

Chair of Computational Vision in Computing, School of Science and Engineering, University of Dundee, and Honorary Clinical Researcher, NHS Tayside

Tom MacGillivray

Dr Tom MacGillivray is an expert in the field of image processing and analysis for clinical research. His team staffs the Image Analysis Core laboratory of the Edinburgh Imaging group joint with the Edinburgh Clinical Research Facility, at the University of Edinburgh where he is a Senior Research Fellow. The laboratory provides specialist support to investigators accessing data from a variety of imaging modalities including MR, CT, PET, ultrasound and retinal imaging. Dr MacGillivray has extensive experience with retinal image processing and analysis with more than 15 years experience facilitating clinical research that features retinal imaging. This includes studies on stroke, cardiovascular disease, MS, diabetes, kidney disease, dementia and age-related cognitive change. In close collaboration with the University of Dundee (Prof E. Trucco, School of Computing), he co-ordinates an interdisciplinary initiative called VAMPIRE (Vascular Assessment and Measurement Platform for Images of the REtina, whose aim is efficient, semi-automatic analysis of retinal images and the pursuit of biomarker identification.

Affiliations and Expertise

Senior Research Fellow, University of Edinburgh, UK

Yanwu Xu

Yanwu Xu (Frank) is the Chief Architect/Scientist of AI Innovation Business Department, Baidu Online Network Technology (Beijing) Co., Ltd. He is also an Adjunct Professor at Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences (CAS). He received the B.Eng. and PhD degrees from the University of Science and Technology of China, in 2004 and 2009, respectively. He worked as a postdoctoral Research Fellow at Nanyang Technological University, Singapore, from 2009 to 2011, a Research Scientist at Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, from 2011 to 2017, and the head of Biomedical Research Department at Central Research Institute, CVTE, from 2017 to 2018. He has published more than 100 papers in international journals and conferences, including T-MI, T-SMCB, JAMIA, MICCAI, etc. He has applied for more than 30 China patents (5 granted) and 11 PCT international patents (5 granted), including two licensed to a NMC and a Singapore startup.

Affiliations and Expertise

Chief Architect/Scientist of AI Innovation Business Department, Baidu Online Network Technology (Beijing) Co., Ltd. and Adjunct Professor, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), China"

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