Retinal Optical Coherence Tomography Image Analysis, 1st ed. 2019
Biological and Medical Physics, Biomedical Engineering Series

Coordinators: Chen Xinjian, Shi Fei, Chen Haoyu

Language: Anglais

137.14 €

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385 p. · 15.5x23.5 cm · Hardback

This book introduces the latest optical coherence tomography (OCT) imaging and computerized automatic image analysis techniques, and their applications in the diagnosis and treatment of retinal diseases. Discussing the basic principles and the clinical applications of OCT imaging, OCT image preprocessing, as well as the automatic detection and quantitative analysis of retinal anatomy and pathology, it includes a wealth of clinical OCT images, and state-of-the-art research that applies novel image processing, pattern recognition and machine learning methods to real clinical data. It is a valuable resource for researchers in both medical image processing and ophthalmic imaging.

Clinical applications of retinal optical coherence tomography.- Fundamentals of optical coherence tomography.- Speckle noise reduction and enhancement for OCT images.- Reconstruction of retinal OCT images with sparse representation.- Segmentation of OCT scans using probabilistic graphical models.- Diagnostic capability of optical coherence tomography based quantitative analysis for various eye diseases and additional factors affecting morphological measurements.- Quantitative analysis of retinal layers' optical intensities based on optical coherence tomography.- Segmentation of optic disc and cup-to-disc ratio quantification based on OCT scans.- Choroidal OCT analytics.- Layer segmentation and analysis for retina with diseases.- Segmentation and visualization of drusen and geographic atrophy in SD-OCT images.- Segmentation of symptomatic exudate-associated derangements in 3D OCT images.- Modeling and prediction of chroidal neovascularization growth based on longitudinal OCT scans.

Xinjian Chen,IEEE Senior Member, received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2006. After graduation, he worked on research projects with several prestigious groups: Microsoft Research Asia, Beijing, China (2006-2007); Medical Image Processing Group, University of Pennsylvania (2008-2009); Department of Radiology and Image Sciences, National Institutes of Health  (2009-2011); and Department of Electrical and Computer Engineering, University of Iowa  (2011-2012). In 2012, he joined the School of Electrical and Information Engineering, Soochow University where he serves as a Distinguished Professor and Director of Medical Image Processing, Analysis and Visualization Laboratory. Under his leadership, the laboratory has evolved with a strong group of 8 faculty members and 30 postgraduate students working towards PhD and MS programs. The laboratory has received more than 10 National and Provincial grants, including the prestigious Young Scientist grant with Xinjian as the PI from National Basic Research Program of China (973).

Dr. Chen is a recipientof the National Science Fund for Outstanding Young Scholars, China (2016), National One Thousand Young Talents Award, China (2012), Jiangsu Provincial High Level Creative Talents Award (2013), Jiangsu Provincial Peak Talents of Six Categories Award (2012), Beijing Science and Technology Advancement Award (2011), Chinese Academy of Sciences President Excellence Award (2006), Important Technology Innovation Award of China (2005), and National Technology Advancement Award of China (2004).His research interests include medical image processing and analysis, pattern recognition, machine learning, and their applications.

Dr. Chen has published more than 100 peer-reviewed papers in prestigious international journals and conferences including IEEE Transactions on Medical Imaging, IEEE Transactions on

Focuses on computerized automatic analysis of clinical optical coherence tomography (OCT) images

Includes a wealth of examples of clinical OCT images for different retinal pathologies

Offers comprehensive and in-depth descriptions of state-of-the-art image analysis methods applied to OCT images