Computational Retinal Image Analysis: Tools, Applications and Perspectives

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.

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

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