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.
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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 Guimar�es 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 Caliv�, 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 Bogunovic, 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 Aur�lio 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
