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Author: Publisher: ISBN: Category : Diabetic retinopathy Languages : en Pages : 426
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: Publisher: ISBN: Category : Diabetic retinopathy Languages : en Pages : 426
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: 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: Herbert Jelinek Publisher: CRC Press ISBN: 1420037005 Category : Technology & Engineering Languages : en Pages : 386
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: 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: Xiayu Xu Publisher: ISBN: Category : Image processing Languages : en Pages : 116
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: Rajendra Acharya U Publisher: Artech House ISBN: 1596932090 Category : Computers Languages : en Pages : 378
Book Description
This groundbreaking resource gives you full details on state-of-the-art 2D and 3D eye imaging and modeling techniques that are paving the way to breakthrough clinical applications in eye health. ItOCOs the first book to explore in depth a new generation of computational methods that combine image processing, simulation, and statistical discrimination tools in efforts to improve early detection of cataracts, diabetic retinopathy, glaucoma, iridocyclitis, corneal haze, maculopathy, and other visual impairments and conditions."
Author: Geoff Dougherty Publisher: Springer Science & Business Media ISBN: 1441997792 Category : Technology & Engineering Languages : en Pages : 388
Book Description
The book is designed for end users in the field of digital imaging, who wish to update their skills and understanding with the latest techniques in image analysis. The book emphasizes the conceptual framework of image analysis and the effective use of image processing tools. It uses applications in a variety of fields to demonstrate and consolidate both specific and general concepts, and to build intuition, insight and understanding. Although the chapters are essentially self-contained they reference other chapters to form an integrated whole. Each chapter employs a pedagogical approach to ensure conceptual learning before introducing specific techniques and “tricks of the trade”. The book concentrates on a number of current research applications, and will present a detailed approach to each while emphasizing the applicability of techniques to other problems. The field of topics is wide, ranging from compressive (non-uniform) sampling in MRI, through automated retinal vessel analysis to 3-D ultrasound imaging and more. The book is amply illustrated with figures and applicable medical images. The reader will learn the techniques which experts in the field are currently employing and testing to solve particular research problems, and how they may be applied to other problems.
Author: Xiaolu Zhu Publisher: Springer Nature ISBN: 3031016491 Category : Technology & Engineering Languages : en Pages : 95
Book Description
Fundus images of the retina are color images of the eye taken by specially designed digital cameras. Ophthalmologists rely on fundus images to diagnose various diseases that affect the eye, such as diabetic retinopathy and retinopathy of prematurity. A crucial preliminary step in the analysis of retinal images is the identification and localization of important anatomical structures, such as the optic nerve head (ONH), the macula, and the major vascular arcades. Identification of the ONH is an important initial step in the detection and analysis of the anatomical structures and pathological features in the retina. Different types of retinal pathology may be detected and analyzed via the application of appropriately designed techniques of digital image processing and pattern recognition. Computer-aided analysis of retinal images has the potential to facilitate quantitative and objective analysis of retinal lesions and abnormalities. Accurate identification and localization of retinal features and lesions could contribute to improved diagnosis, treatment, and management of retinopathy. This book presents an introduction to diagnostic imaging of the retina and an overview of image processing techniques for ophthalmology. In particular, digital image processing algorithms and pattern analysis techniques for the detection of the ONH are described. In fundus images, the ONH usually appears as a bright region, white or yellow in color, and is indicated as the convergent area of the network of blood vessels. Use of the geometrical and intensity characteristics of the ONH, as well as the property that the ONH represents the location of entrance of the blood vessels and the optic nerve into the retina, is demonstrated in developing the methods. The image processing techniques described in the book include morphological filters for preprocessing fundus images, filters for edge detection, the Hough transform for the detection of lines and circles, Gabor filters to detect the blood vessels, and phase portrait analysis for the detection of convergent or node-like patterns. Illustrations of application of the methods to fundus images from two publicly available databases are presented, in terms of locating the center and the boundary of the ONH. Methods for quantitative evaluation of the results of detection of the ONH using measures of overlap and free-response receiver operating characteristics are also described. Table of Contents: Introduction / Computer-aided Analysis of Images of the Retina / Detection of Geometrical Patterns / Datasets and Experimental Setup / Detection of the\\Optic Nerve Head\\Using the Hough Transform / Detection of the\\Optic Nerve Head\\Using Phase Portraits / Concluding Remarks
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Author: Emanuele Trucco
Author: Herbert Jelinek
Author: Rashmi Turior
Author: Danu Onkaew
Author: Esra Ataer-Cansizoglu
Author: Xiayu Xu
Author: Rajendra Acharya U
Author: Geoff Dougherty
Author: Xiaolu Zhu