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Retinal Optical Coherence Tomography Image Analysis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Retinal Optical Coherence Tomography Image Analysis/ edited by Xinjian Chen, Fei Shi, Haoyu Chen.
other author:	Chen, Xinjian.
Description:	XI, 385 p. 189 illus., 156 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Lasers. -
Online resource:	https://doi.org/10.1007/978-981-13-1825-2
ISBN:	9789811318252

Retinal Optical Coherence Tomography Image Analysis
Retinal Optical Coherence Tomography Image Analysis[electronic resource] /edited by Xinjian Chen, Fei Shi, Haoyu Chen. - 1st ed. 2019. - XI, 385 p. 189 illus., 156 illus. in color.online resource. - Biological and Medical Physics, Biomedical Engineering,1618-7210. - Biological and Medical Physics, Biomedical Engineering,.

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.

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.

ISBN: 9789811318252

Standard No.: 10.1007/978-981-13-1825-2doiSubjects--Topical Terms:

557748
Lasers.

LC Class. No.: TA1671-1707

Dewey Class. No.: 621.36

Retinal Optical Coherence Tomography Image Analysis
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505 0 $a 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.
520 $a 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.
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