Description
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 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
Table of Contents
CHAPTER 1 A brief introduction and a glimpse into the pastEmanuele Trucco, Yanwu Xu, and Tom MacGillivrayCHAPTER 2 Clinical motivation and the needs for RIAin healthcare Ryo Kawasaki and Jakob GrauslundCHAPTER 3 The physics, instruments and modalitiesof retinal imagingAndrew R. Harvey, Guillem Carles, Adrian Bradu andAdrian PodoleanuCHAPTER 4 Retinal image preprocessing, enhancement,and registrationCarlos Hernandez-Matas, Antonis A. Argyrosand Xenophon ZabulisCHAPTER 5 Automatic landmark detection in fundusphotographyJeffrey Wigdahl, Pedro Guimarães and Alfredo RuggeriCHAPTER 6 Retinal vascular analysis: Segmentation,tracing, and beyond Li Cheng, Xingzheng Lyu, He Zhao, Huazhu Fuand Huiqi LiCHAPTER 7 OCT layer segmentation Sandro De Zanet, Carlos Ciller, Stefanos Apostolopoulos,Sebastian Wolf and Raphael SznitmanCHAPTER 8 Image quality assessment Sarah A. Barman, Roshan A. Welikala, Alicja R. Rudnickaand Christopher G. OwenCHAPTER 9 Validation Emanuele Trucco, Andrew McNeil, Sarah McGrory, LuciaBallerini, Muthu Rama Krishnan Mookiah, Stephen Hogg,Alexander Doney and Tom MacGillivrayCHAPTER 10 Statistical analysis and design inophthalmology: Toward optimizing your data Gabriela Czanner and Catey BunceCHAPTER 11 Structure-preserving guided retinalimage filtering for optic disc analysis Jun Cheng, Zhengguo Li, Zaiwang Gu, Huazhu Fu,Damon Wing Kee Wong and Jiang LiuCHAPTER 12 Diabetic retinopathy and maculopathy lesions Bashir Al-Diri, Francesco Calivá, Piotr Chudzik,Giovanni Ometto and Maged HabibCHAPTER 13 Drusen and macular degeneration Bryan M. Williams, Philip I. Burgess andYalin ZhengCHAPTER 14 OCT fluid detection and quantification Hrvoje Bogunović, Wolf-Dieter Vogl,Sebastian M. Waldstein and Ursula Schmidt-ErfurthCHAPTER 15 Retinal biomarkers and cardiovasculardisease: A clinical perspective Carol Yim-lui Cheung, Posey Po-yin Wong andTien Yin WongCHAPTER 16 Vascular biomarkers for diabetesand diabetic retinopathy screening Fan Huang, Samaneh Abbasi-Sureshjani, Jiong Zhang,Erik J. Bekkers, Behdad Dashtbozorg andBart M. ter Haar RomenyCHAPTER 17 Image analysis tools for assessment of atrophicmacular diseases Zhihong Jewel Hu and Srinivas Reddy SaddaCHAPTER 18 Artificial intelligence and deep learningin retinal image analysis Philippe Burlina, Adrian Galdran, Pedro Costa, AdamCohen and Aurélio CampilhoCHAPTER 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 LaCHAPTER 20 The challenges of assembling, maintainingand making available large data setsof clinical data for research Emily R. Jefferson and Emanuele TruccoCHAPTER 21 Technical and clinical challengesof A.I. in retinal image analysis Gilbert Lim, Wynne Hsu, Mong Li Lee, Daniel Shu WeiTing and Tien Yin Wong
