Automated Delineation and Quantitative Analysis of Blood Vessels in Retinal Fundus Image PDF Download

Author: Xiayu Xu
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 116

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Book Description
Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many studies has been done to explore these relationships. However, most of the studies are based on limited data obtained using manual or semi-automated methods due to the lack of automated techniques in the measurement and analysis of retinal vasculature. In this thesis, a fully automated retinal vessel width measurement technique is proposed. This novel method models the accurate vessel boundary delineation problem in two-dimension into an optimal surface segmentation problem in threedimension. Then the optimal surface segmentation problem is transformed into finding a minimum-cost closed set problem in a vertex-weighted geometric graph. The problem is modeled differently for straight vessel and for branch point because of the different conditions in straight vessel and in branch point. Furthermore, many of the retinal image analysis needs the location of the optic disc and fovea as a prerequisite information, for example, in the analysis of the relationship between vessel width and the distance to the optic disc. Hence, a simultaneous optic disc and fovea detection method is presented, which includes a two-step classification of three classes. The major contributions of this thesis include: 1) developing a fully automated vessel width measurement technique for retinal blood vessels, 2) developing a simultaneous optic disc and fovea detection method, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies.

Author: Ali Hatamizadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 55

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Book Description
The reliable segmentation and quantification of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. In this thesis, we address this problem in depth, leveraging the power of artificial intelligence to devise automated approaches for the segmentation and width estimation of vessels in two ophthalmological image modalities. First, we investigate the automated segmentation of retinal vessels in color fundus images. We propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder. We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions. The proposed methodology takes a whole-image approach and is tested on two publicly available datasets, DRIVE and CHASE-DB1. Second, we introduce the first deep-learning based method for the semantic segmentation of retinal arteries and veins in infrared imaging along with a novel dataset dubbed AVIR, and propose an innovative encoder-decoder that is regularized by variational autoencoders. Additionally, our method automatically quantifies the morphological changes of the segmented arteries and veins, which is important for establishing automated vessel tracking systems.

Author: Herbert Jelinek
Publisher: CRC Press
ISBN: 1420037005
Category : Technology & Engineering
Languages : en
Pages : 386

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Book Description
Discusses the Effect of Automated Assessment Programs on Health Care ProvisionDiabetes is approaching pandemic numbers, and as an associated complication, diabetic retinopathy is also on the rise. Much about the computer-based diagnosis of this intricate illness has been discovered and proven effective in research labs. But, unfortunately, many of

Author: Emanuele Trucco
Publisher: Academic Press
ISBN: 0081028164
Category : Computers
Languages : en
Pages : 504

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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: Rubiel Vargas Canas
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
This thesis presents an automated framework for quantitative analysis of fluorescein angiographies of the human retina. Such framework represents the core of a computer-aided system, which can assist NHS clinicians in early diagnosis of macular degeneration (AMD). The presented methodology aims to demonstrate the technical feasibility of automated extraction of retinal blood flow parameters, and results in a step forward in the development of an automated computer vision system for quantitative analysis of fluorescein angiograms to assist NHS clinicians in early diagnosis of AMD. The approach commences by segmenting the anatomic constituent parts of the ocular fundus, i.e. the optic disc (OD), the fovea and the vascular network tree. The OD/fovea are simultaneously detected by combining luminance information and geometric information from the major blood vessels; information regarding OD/fovea is then used for delineating the macula. Meanwhile, to segment the retinal vasculature, three independent approaches are implemented. These approaches use information about maximum curvature in both image- and frequency-domains. Such information is combined, firstly, using a supervised linear classifier. Secondly, utilising a committee of local experts, where each expert is represented by an artificial neural network. A fuzzy clustering algorithm is used for expert selection based on the specific input pattern. The output of the system is determined through a winner-takes-all rule. And finally. by considering a tracing algorithm that follows vessel centrelines and walls using a set of rules based upon information of maximum curvature and symmetry. Following segmentation, the extracted vasculature is utilised as input features for a multi modal registration algorithm, which has its fundamentals on the Fourier transform and a parametric estimation based on the gradient of the quadratic error function and least squares computation. Once subsequent frames of the angiogram have been aligned, anatomic, morphologic and sequential analyses are carried out. Special attention is given to the latter one, which is a methodology for quantitative analysis of retinal haemodynamics. It analyses retinal blood flow based on the estimation of parameters such as mean transit-time (MTT) and vascular volume. The former parameter is estimated using densitometry and analysis of the vascular response; the latter is calculated from the lumen of extracted vessels. The performance of the framework is demonstrated on a comprehensive dataset, which contains images of normal retinas and retinas with pathologies such as wet age-related macular degeneration and branch retinal vein occlusion. Results achieved in certain individual modules overcame serious defects observed in previous methods.

Author: G.V. Shrichandran
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 15

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Book Description
Retinal blood vessels segmentation is mandatory for retinal image analysis, diagnose and treatment of specific diseases. However, the manual analysis of the retinal image is time consuming, therefore, it is necessary for developing an automatic analysis of retinal fundus images.

Author:
Publisher:
ISBN:
Category : Diabetic retinopathy
Languages : en
Pages : 426

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Book Description
In the last few decades, an automated retinal image analysis for a diabetic retinopathy has been a major area of attention in the computer vision. The typical approach used by Ophthalmologists for examining the eye is the pupil dilation. This takes time, is not accurate, and is uncomfortable for patients. On the other hand, the automated retinal image analysis for retina pathologies is more sophisticated technology by which Ophthalmologists could screen the retina of the eye regularly and find out its normal and abnormal structures in a more precise and comfortable way. Monitoring the retina of the eye, utilizing an automatic method, and by applying necessary cure in advance could save patients from losing their vision. In recent time, there were many research works on automated detection and classification of the features of the eye in the fundus [normal structures and abnormal structures (retina pathologies)] using different strategies and algorithms to obtain precise results. But they still do not meet many of the requirements. In this research we consider the retinal images taken from non-dilated eye pupils to eliminate the dilation process. These images are noisy, lower in contrast, lower in intensity, and have more non-uniform luminosity due to a non-dilation process and retinal camera. The contributions of this research are robust algorithms and methods that detect and extract as well as measure the landmark features of the retina such as the optic disc, and blood vessels as well as the abnormal structures such as blood vessel tortuosity, hard exudates and soft exudates (cotton wool spots), and an age-related macular degeneration (drusens). This provides early detection and monitoring of retina pathologies for a patient that can be cured by ophthalmologists prior to blindness. We investigated our developed algorithm by applying it to a number of retinal images with noise, low intensity, less color contrast, and non-uniform luminosity which are taken from non-dilated eye pupil. In addition to that, these images carry distinct kinds of retina pathologies such as exudates, drusens, and tortuosity.

Author: Andrzej Grzybowski
Publisher: Springer Nature
ISBN: 3030786013
Category : Medical
Languages : en
Pages : 280

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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: Josef F. Bille
Publisher: Springer
ISBN: 3030166384
Category : Medical
Languages : en
Pages : 407

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Book Description
This open access book provides a comprehensive overview of the application of the newest laser and microscope/ophthalmoscope technology in the field of high resolution imaging in microscopy and ophthalmology. Starting by describing High-Resolution 3D Light Microscopy with STED and RESOLFT, the book goes on to cover retinal and anterior segment imaging and image-guided treatment and also discusses the development of adaptive optics in vision science and ophthalmology. Using an interdisciplinary approach, the reader will learn about the latest developments and most up to date technology in the field and how these translate to a medical setting. High Resolution Imaging in Microscopy and Ophthalmology – New Frontiers in Biomedical Optics has been written by leading experts in the field and offers insights on engineering, biology, and medicine, thus being a valuable addition for scientists, engineers, and clinicians with technical and medical interest who would like to understand the equipment, the applications and the medical/biological background. Lastly, this book is dedicated to the memory of Dr. Gerhard Zinser, co-founder of Heidelberg Engineering GmbH, a scientist, a husband, a brother, a colleague, and a friend.

Author: Stephen J. Ryan
Publisher: Elsevier Health Sciences
ISBN: 1455707376
Category : Retina
Languages : en
Pages : 2498

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Book Description
Unequalled in scope, depth, and clinical precision, Retina, 5th Edition keeps you at the forefront of today's new technologies, surgical approaches, and diagnostic and therapeutic options for retinal diseases and disorders. Comprehensively updated to reflect everything you need to know regarding retinal diagnosis, treatment, development, structure, function, and pathophysiology, this monumental ophthalmology reference work equips you with expert answers to virtually any question you may face in practice. Benefit from the extensive knowledge and experience of esteemed editor Dr. Stephen Ryan, five expert co-editors, and a truly global perspective from 358 other world authorities across Europe, Asia, Australasia the Americas.Examine and evaluate the newest diagnostic technologies and approaches that are changing the management of retinal disease, including future technologies which will soon become the standard.Put the very latest scientific and genetic discoveries, diagnostic imaging methods, drug therapies, treatment recommendations, and surgical techniques to work in your practice.