Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computational Retinal Image Analysis PDF full book. Access full book title Computational Retinal Image Analysis by Emanuele Trucco. Download full books in PDF and EPUB format.
Author: Emanuele Trucco Publisher: Academic Press ISBN: 0081028164 Category : Computers Languages : en Pages : 504
Book 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
Author: Emanuele Trucco Publisher: Academic Press ISBN: 0081028164 Category : Computers Languages : en Pages : 504
Book 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
Author: Emanuele Trucco Publisher: Academic Press ISBN: 0081028172 Category : Computers Languages : en Pages : 506
Book 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
Author: Alexander Y. Lau Publisher: ISBN: Category : Languages : en Pages :
Book Description
Background Retinal imaging with artificial-intelligence assisted analysis has the potential to become a simple and reliable tool for screening population-at-risk of cerebrovascular disease and dementia. ObjectiveTo develop an algorithm with automatic retinal imaging in identifying asymptomatic subjects with high burden of white matter hyperintensities (WMH).MethodsWe performed automated retinal image analysis (ARIA) in 180 community dwelling, stroke and dementia-free healthy subjects. ARIA is fully automatic and validated in separate disease cohorts. WMH on MRI brain was graded using ARWMC scale by an independent accessor. 126(70%) subjects were randomly selected for model building, 27(15%) for model cross-validation, and remaining 27(15%) for testing; all 180 subjects were used for evaluation of model accuracy to predict WMH burden. ResultsAll 180 subjects completed ARIA with successful analysis. The mean age was 70.3 +/- 4.5 years, 70(39%) were male. Risk factor profiles were: 106(59%) hypertension, 31(17%) diabetes, and 47(26%) hyperlipidemia. Severe WMH (defined as global ARWMC grading >=2) was found in 56(31%) subjects. The performance (sensitivity, SN; and specificity, SP) for model building (SN 96.7%, SP 80.6%), model validation (SN 100%, SP 87.5%), and testing (SN 100%, SP 83.3%) was excellent. The overall performance was SN 97.6% and SP 82.1%, with PPV 94% and NPV 92%. There was good correlation with WMH volume (log-transformed) in the building (R=0.92), validation (R=0.81), testing (R=0.88) and overall (R=0.90) models, respectively. DiscussionWe developed a robust algorithm to automatically evaluate retinal fundus image that can identify community subjects with high WMH burden.
Author: Xinjian Chen Publisher: Springer ISBN: 9811318255 Category : Science Languages : en Pages : 387
Book Description
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.
Author: Kolekar, Maheshkumar H. Publisher: IGI Global ISBN: 152252830X Category : Technology & Engineering Languages : en Pages : 333
Book Description
In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions.
Author: Eddie Y. K. Ng Publisher: CRC Press ISBN: 1466559306 Category : Technology & Engineering Languages : en Pages : 412
Book Description
Digital fundus images can effectively diagnose glaucoma and diabetes retinopathy, while infrared imaging can show changes in the vascular tissues. Likening the eye to the conventional camera, Image Analysis and Modeling in Ophthalmology explores the application of advanced image processing in ocular imaging. This book considers how images can be used to effectively diagnose ophthalmologic problems. It introduces multi-modality image processing algorithms as a means for analyzing subtle changes in the eye. It details eye imaging, textural imaging, and modeling, and highlights specific imaging and modeling techniques. The book covers the detection of diabetes retinopathy, glaucoma, anterior segment eye abnormalities, instruments on detection of glaucoma, and development of human eye models using computational fluid dynamics and heat transfer principles to predict inner temperatures of the eye from its surface temperature. It presents an ultrasound biomicroscopy (UBM) system for anterior chamber angle imaging and proposes an automated anterior segment eye disease classification system that can be used for early disease diagnosis and treatment management. It focuses on the segmentation of the blood vessels in high-resolution retinal images and describes the integration of the image processing methodologies in a web-based framework aimed at retinal analysis. The authors introduce the A-Levelset algorithm, explore the ARGALI system to calculate the cup-to-disc ratio (CDR), and describe the Singapore Eye Vessel Assessment (SIVA) system, a holistic tool which brings together various technologies from image processing and artificial intelligence to construct vascular models from retinal images. The text furnishes the working principles of mechanical and optical instruments for the diagnosis and healthcare administration of glaucoma, reviews state-of-the-art CDR calculation detail, and discusses the existing methods and databases. Image Analysis and Modeling in Ophthalmology includes the latest research development in the field of eye modeling and the multi-modality image processing techniques in ocular imaging. It addresses the differences, performance measures, advantages and disadvantages of various approaches, and provides extensive reviews on related fields.
Author: Esra Ataer-Cansizoglu Publisher: ISBN: Category : Image analysis Languages : en Pages : 107
Book Description
The need for computerized analysis of retinal images has been increasing with the wide clinical use of fundus photography. Retinopathy of prematurity (ROP) is among the diseases that can be diagnosed through the use of retinal images. It is a disease affecting low-birth weight infants, in which blood vessels in the retina of the eye develop abnormally and cause potential blindness. We propose an image processing and machine learning framework from the vasculature segmentation to diagnosis of Retinopathy of Prematurity (ROP) in retinal images. The system takes a retinal image as an input, does automatic vessel segmentation and tracing, extracts various features, performs feature selection and outputs a diagnostic decision. Although, ROP is the leading cause of childhood blindness in the world, there exists a wide variability among experts in diagnosis. We propose a method to do an in-depth feature and observer analysis by employing Mutual Information (MI) to understand the underlying causes of inter-expert disagreement. The contributions of this dissertation are (i) extraction of new features quantifying tortuosity and amount of branching that are useful for ROP diagnosis, (ii) a novel feature representation paradigm utilizing Gaussian Mixture Models of image features to better model tortuous and straight vessels, (iii) an accurate pairwise similarity measure between images based on the proposed feature representation, (iv) the use of the proposed similarity measure in support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, and (v) a MI-based feature-observer analysis technique to understand the features that lead inter-expert disagreement. The proposed framework is the first fully-automated computer-aided diagnosis system for ROP disease. The experiments are carried out on two datasets each of which consists of wide-angle colored retinal images acquired during routine ROP exams. The first one is designed for feature-observer analysis and consists of 34 images diagnosed by 22 experts. The second one is designed for classification experiments and contains 77 images with reference standard diagnosis. In our feature-observer analysis, we observed that although ROP is defined based on arteriolar tortuosity and venous dilation, there exists other features that highly correlate with expert opinions. This finding shows that the definition of the disease is subjective. We obtain 95\% accuracy compared to the reference standard on the second dataset when we use features extracted from manual segmentations. This performance is comparable to the performance of the individual experts (96%, 93%, 92%), Williams test = 1.0. With the features extracted from computer-based segmentation algorithm, we achieve 80% accuracy, which is promising for a fully-automated system.
Author: Andrzej Grzybowski Publisher: Springer Nature ISBN: 3030786013 Category : Medical Languages : en Pages : 280
Book Description
This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.
Author: Andrés G. Marrugo Hernández Publisher: ISBN: Category : Languages : en Pages : 159
Book Description
Medical digital imaging has become a key element of modern health care procedures. It provides a visual documentation, a permanent record for the patients, and most importantly the ability to extract information about many diseases. Ophthalmology is a field that is heavily dependent on the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation contributes to the digital analysis of such images and the problems that arise along the imaging pipeline, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination, poor image quality, automated focusing, and multichannel analysis. However, there are many unavoidable situations in which images of poor quality, like blurred retinal images because of aberrations in the eye, are acquired. To address this problem we have proposed two approaches for blind deconvolution of blurred retinal images. In the first approach, we consider the blur to be space-invariant and later in the second approach we extend the work and propose a more general space-variant scheme. For the development of the algorithms we have built preprocessing solutions that have enabled the extraction of retinal features of medical relevancy, like the segmentation of the optic disc and the detection and visualization of longitudinal structural changes in the retina. Encouraging experimental results carried out on real retinal images coming from the clinical setting demonstrate the applicability of our proposed solutions.
Author: Giovanna Guidoboni Publisher: Springer Nature ISBN: 3030258866 Category : Mathematics Languages : en Pages : 606
Book Description
The chapters in this contributed volume showcase current theoretical approaches in the modeling of ocular fluid dynamics in health and disease. By including chapters written by experts from a variety of fields, this volume will help foster a genuinely collaborative spirit between clinical and research scientists. It vividly illustrates the advantages of clinical and experimental methods, data-driven modeling, and physically-based modeling, while also detailing the limitations of each approach. Blood, aqueous humor, vitreous humor, tear film, and cerebrospinal fluid each have a section dedicated to their anatomy and physiology, pathological conditions, imaging techniques, and mathematical modeling. Because each fluid receives a thorough analysis from experts in their respective fields, this volume stands out among the existing ophthalmology literature. Ocular Fluid Dynamics is ideal for current and future graduate students in applied mathematics and ophthalmology who wish to explore the field by investigating open questions, experimental technologies, and mathematical models. It will also be a valuable resource for researchers in mathematics, engineering, physics, computer science, chemistry, ophthalmology, and more.
Author: Emanuele Trucco
Author: Emanuele Trucco
Author: Emanuele Trucco
Author: Alexander Y. Lau
Author: Xinjian Chen
Author: Kolekar, Maheshkumar H.
Author: Eddie Y. K. Ng
Author: Esra Ataer-Cansizoglu
Author: Andrzej Grzybowski
Author: Andrés G. Marrugo Hernández
Author: Giovanna Guidoboni