Freshly Printed - allow 10 days lead
Couldn't load pickup availability
Computational Retinal Image Analysis
Tools, Applications and Perspectives
A reference on the latest research and current techniques in retinal image analysis that also includes insights into future directions
Emanuele Trucco (Edited by), Tom MacGillivray (Edited by), Yanwu Xu (Edited by)
9780081028162
Paperback / softback, published 20 November 2019
502 pages
23.4 x 19 x 3.1 cm, 1.04 kg
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.
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 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 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
Subject Areas: Image processing [UYT], Enterprise software [UFL], Medical bioinformatics [MBF]
