About the Book
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.
Table of Contents:
CHAPTER 1 A brief introduction and a glimpse into the past
Emanuele Trucco, Yanwu Xu, and Tom MacGillivray
CHAPTER 2 Clinical motivation and the needs for RIA
in healthcare
Ryo Kawasaki and Jakob Grauslund
CHAPTER 3 The physics, instruments and modalities
of retinal imaging
Andrew R. Harvey, Guillem Carles, Adrian Bradu and
Adrian Podoleanu
CHAPTER 4 Retinal image preprocessing, enhancement,
and registration
Carlos Hernandez-Matas, Antonis A. Argyros
and Xenophon Zabulis
CHAPTER 5 Automatic landmark detection in fundus
photography
Jeffrey Wigdahl, Pedro Guimarães and Alfredo Ruggeri
CHAPTER 6 Retinal vascular analysis: Segmentation,
tracing, and beyond
Li Cheng, Xingzheng Lyu, He Zhao, Huazhu Fu
and Huiqi Li
CHAPTER 7 OCT layer segmentation
Sandro De Zanet, Carlos Ciller, Stefanos Apostolopoulos,
Sebastian Wolf and Raphael Sznitman
CHAPTER 8 Image quality assessment
Sarah A. Barman, Roshan A. Welikala, Alicja R. Rudnicka
and Christopher G. Owen
CHAPTER 9 Validation
Emanuele Trucco, Andrew McNeil, Sarah McGrory, Lucia
Ballerini, Muthu Rama Krishnan Mookiah, Stephen Hogg,
Alexander Doney and Tom MacGillivray
CHAPTER 10 Statistical analysis and design in
ophthalmology: Toward optimizing your data
Gabriela Czanner and Catey Bunce
CHAPTER 11 Structure-preserving guided retinal
image filtering for optic disc analysis
Jun Cheng, Zhengguo Li, Zaiwang Gu, Huazhu Fu,
Damon Wing Kee Wong and Jiang Liu
CHAPTER 12 Diabetic retinopathy and maculopathy lesions
Bashir Al-Diri, Francesco Calivá, Piotr Chudzik,
Giovanni Ometto and Maged Habib
CHAPTER 13 Drusen and macular degeneration
Bryan M. Williams, Philip I. Burgess and
Yalin Zheng
CHAPTER 14 OCT fluid detection and quantification
Hrvoje Bogunović, Wolf-Dieter Vogl,
Sebastian M. Waldstein and Ursula Schmidt-Erfurth
CHAPTER 15 Retinal biomarkers and cardiovascular
disease: A clinical perspective
Carol Yim-lui Cheung, Posey Po-yin Wong and
Tien Yin Wong
CHAPTER 16 Vascular biomarkers for diabetes
and diabetic retinopathy screening
Fan Huang, Samaneh Abbasi-Sureshjani, Jiong Zhang,
Erik J. Bekkers, Behdad Dashtbozorg and
Bart M. ter Haar Romeny
CHAPTER 17 Image analysis tools for assessment of atrophic
macular diseases
Zhihong Jewel Hu and Srinivas Reddy Sadda
CHAPTER 18 Artificial intelligence and deep learning
in retinal image analysis
Philippe Burlina, Adrian Galdran, Pedro Costa, Adam
Cohen and Aurélio Campilho
CHAPTER 19 AI and retinal image analysis at Baidu
Yehui Yang, Dalu Yang, Yanwu Xu, Lei Wang,
Yan Huang, Xing Li, Xuan Liu and Le Van La
CHAPTER 20 The challenges of assembling, maintaining
and making available large data sets
of clinical data for research
Emily R. Jefferson and Emanuele Trucco
CHAPTER 21 Technical and clinical challenges
of A.I. in retinal image analysis
Gilbert Lim, Wynne Hsu, Mong Li Lee, Daniel Shu Wei
Ting and Tien Yin Wong
