Download or read book Computational Retinal Image Analysis written by Emanuele Trucco and published by Academic Press. This book was released on 2019-12-04 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Computational Retinal Image Analysis written by Emanuele Trucco and published by Academic Press. This book was released on 2019-11-19 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book AUTOMATED RETINAL IMAGE ANALYSIS TO DETECT WHITE MATTER HYPERINTENSITIES IN STROKE AND DEMENTIA FREE HEALTHY SUBJECTS A CROSS VALIDATION STUDY written by Alexander Y. Lau and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Image Analysis and Modeling in Ophthalmology written by Eddie Y. K. Ng and published by CRC Press. This book was released on 2014-02-11 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Successful thermal modeling of the human eye helps in the early diagnosis of eye abnormalities such as inflammation, cataracts, diabetic retinopathy, and glaucoma-all leading causes of blindness. This book presents a unified work of eye imaging and modeling techniques that have been proposed and applied to ophthalmologic problems. It delves into various morphological, texture, higher order spectra, and wavelet transformation techniques used to extract important diagnostic features from images, which can then be analyzed by a data scientist for automated diagnosis.

Download or read book Signal Processing and Machine Learning for Biomedical Big Data written by Ervin Sejdic and published by CRC Press. This book was released on 2018-07-04 with total page 1235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Download or read book Image Analysis and Modeling in Ophthalmology written by Eddie Y. K. Ng and published by CRC Press. This book was released on 2014-02-11 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Retinal Image Analytics written by Esra Ataer-Cansizoglu and published by . This book was released on 2015 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Biomedical Signal and Image Processing in Patient Care written by Kolekar, Maheshkumar H. and published by IGI Global. This book was released on 2017-08-11 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Retinal Optical Coherence Tomography Image Analysis written by Xinjian Chen and published by Springer. This book was released on 2019-07-05 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Comprehensive Retinal Image Analysis Image Processing and Feature Extraction Techniques Oriented to the Clinical Task written by Andrés G. Marrugo Hernández and published by . This book was released on 2014 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence in Ophthalmology written by Andrzej Grzybowski and published by Springer Nature. This book was released on 2021-10-13 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Image Analysis and Recognition written by Mohamed Kamel and published by Springer. This book was released on 2013-06-05 with total page 828 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Image Analysis and Recognition, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The 92 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in topical sections on biometrics: behavioral; biometrics: physiological; classification and regression; object recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image analysis; tracking; medical imaging: image segmentation, image registration, image analysis, coronary image analysis, retinal image analysis, computer aided diagnosis, brain image analysis; cell image analysis; RGB-D camera applications; methods of moments; applications.

Download or read book Retinal Imaging written by David Huang and published by Mosby. This book was released on 2006 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: SECTION I: IMAGING MODALITIES: BASIC PRINCIPLES & INTERPRETATION -- 1. Fluorescein angiography -- 2. Indocyanine green angiography -- 3. Optical coherence tomography (OCT) -- -- 4. Optical coherence tomographic ophthalmoscopy -- 5. Ultrasound -- 6. Scanning laser tomography -- 7. Scanning laser polarimetry -- 8. Retinal thickness analyzer -- 9. Adaptive optics ophthalmoscopy -- 10. Imaging of Ocular Blood Flow -- SECTION II: MACULAR DISEASES -- 11. Non-neovascular age-related macular degeneration -- -- 12. Neovascular Age-Related Macular Degeneration -- 13. Pathologic myopia -- 14. Central serous retinopathy -- 15. Macular holes -- 16. Epiretinal membranes -- 17. Macular dystrophies -- 18. Cystoid macular edema -- 19. Angiod streaks -- 20. Chrorioretinal folds -- SECTION III: RETINAL VASCULAR DISEASES -- 21. Diabetic Retinopathy -- 22. Arterial obstructive disease -- 23. Venous obstructive disease -- 24. Parafoveal Telangiectasis -- 25. Coats' disease -- 26. Retinopathy of prematurity -- 27. Ocular ischemic syndrome -- 28. Hypertensive retinopathy -- 29. Radiation retinopathy -- 30. Retinal artery macroanuerysm -- SECTION IV: INFLAMMATORY & INFECTIOUS DISEASES -- 31. Posterior Scleritis -- -- 32. Pars Planitis -- 33. Sarcoidosis -- 34. Uveal Effusion Syndrome -- 35. White Dot Syndromes -- -- 36. Sympathetic Ophthalmia -- 37. Vogt-Koyanagi-Harada Disease -- 38. Syphilis -- 39. Tuberculosis -- 40. Ocular Histoplasmosis -- 41. Fungal Infections -- 42. Endophthalmitis -- 43. Acute Retinal Necrosis -- 44. Toxoplasmosis -- 45. Toxocariasis -- 46. Cysticercosis -- 47. Diffuse Unilateral Subacute Neuroretinitis -- 48. Cytomegalovirus Retinitis -- SECTION V: OTHER RETINAL DISEASES -- 49. Ocular Phototoxicity -- 50. Metabolic and nutritional anomalies -- 51. Medications and Retinal Toxicity -- 52. Retinal injuries -- 53. Hereditary/congenital vitreoretinal disorders -- 54. Retinitis pigmentosa and allied disorders -- SECTION VI: TUMORS -- 55. Retinoblastoma -- 56. Choroidal malignant melanoma -- 57. Choroidal nevus -- 58. Cavernous hemangioma of the retina -- 59. Retinal capillary hemangioma -- 60. Choroidal hemangioma -- -- 61. Tuberous sclerosis complex -- 62. Tumors and related lesions of the retinal pigment epithelium -- 63. Congenital hypertrophy of the retinal pigment epithelium and other pigmented lesions -- 64. Choroidal/retinal metastasis -- 65. Osteomas -- 66. Leukemia /lymphomas -- SECTION VII: OPTIC NERVE DISORDERS -- 67. Optic pits -- 68. Optic nerve head drusen -- 69. Melanocytoma of the optic disc -- 70. Papilledema -- 71. Glaucoma -- 72. Other optic nerve malformations.

Download or read book Digital Image Processing for Ophthalmology written by Xiaolu Zhu and published by Morgan & Claypool Publishers. This book was released on 2011-02-02 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Ryan s Retinal Imaging and Diagnostics E Book written by Stephen J. Ryan and published by Elsevier Health Sciences. This book was released on 2013-08-13 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: Access all of the latest advances in imaging techniques of the retina and posterior segment on your favorite eReader with Ryan's Retinal Imaging and Diagnostics eBook. 12 chapters from the landmark reference Retina, 5th Edition offer the foundations to better understand, apply, and optimize new and emerging retinal imaging technologies. Examine and evaluate the newest diagnostic technologies and approaches that are changing the management of retinal disease, including future imaging technologies which will soon become the standard. Put the very latest diagnostic imaging methods to work in your practice, including optical coherence tomography (OCT), fluoroscein angiography, indocyanine angiography autofluorescence imaging, ophthalmic ultrasound and more. Benefit from the extensive knowledge and experience of esteemed editor and ophthalmologist, the late Dr. Stephen Ryan, and a truly global perspective from the world authorities. Consult this title on your favorite e-reader, conduct rapid searches, and adjust font sizes for optimal readability. Compatible with Kindle®, nook®, and other popular devices.

Download or read book Ocular Fluid Dynamics written by Giovanna Guidoboni and published by Springer Nature. This book was released on 2019-11-25 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automatic Retinal Image Analysis to Triage Retinal Pathologies written by Renoh Johnson Chalakkal and published by . This book was released on 2019 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundus retinal imaging is a non-invasive way of imaging the retina popular among the ophthalmic community and the targeted population. Over the past 15 years, extensive research and clinical studies using fundus images have been done for automatizing the screening and diagnosing process of three significant conditions affecting vision: macular edema, diabetic retinopathy, and glaucoma. These are the most important causes of preventable blindness around the globe, yet they can be successfully screened using the fundus image of the retina. Such diseases are associated with an observable variation in the structural and functional properties of the retina. Manual triage/diagnosis of these diseases is time-consuming and requires specialized ophthalmologists/optometrists; it is also expensive. Computer-aided medical triage/diagnosis can be applied to fundus retinal image analysis, thereby automatizing the triage. The process involves successfully combining sub-tasks focused at analyzing, locating, and segmenting different landmark structures inside a retina. The preliminary objective of this thesis is to develop automatic retinal image analysis (ARIA) techniques capable of analyzing, locating, and segmenting the key structures from the fundus image and combine them effectively to create a complete automatic screening system. First, the retinal vessel, which is the most important structure, is segmented. Two methods are developed for doing this: the first uses adaptive histogram equalization and anisotropic diffusion filtering, followed by weighted scaling and vessel edge enhancement. Fuzzy-C-mean classification, together with morphological transforms and connected component analysis, is applied to segment the vessel pixels. A second improved method for vessel segmentation is proposed, which is capable of segmenting the tiny peripheral vessel pixels missed by the first method. This method uses curvelet transform-based vessel edge enhancement technique followed by modified line operator-based vessel pixel segmentation. Second, a novel technique to automatically detect and segment important structures such as optic disc, macula, and fovea from a retinal image is developed. These structures, together with the retinal vessels, are considered as the retinal landmarks. The proposed method automatically detects the optic disc using histogram-based template matching combined with the maximum sum of vessel information. The optic disc region is segmented by using the Circular Hough Transform. For detecting fovea, the retinal image is uniformly divided into three horizontal stripes, and the strip including the detected optic disc, is selected. The contrast of the horizontal strip containing the optic disc region is then enhanced using a series of image processing steps. The macula region is first detected in the optic disc strip using various morphological operations and connected component analysis. The fovea is located inside this detected macular region. Next, an algorithm capable of analyzing the retinal image quality and content is developed. Often, methods focusing on ARIA use public retinal image databases for performance evaluation. The quality of images in such databases is often not evaluated as a pre-requisite for ARIA. Therefore, the performance metrics reported by such ARIA methods are inconsistent. Considering these facts, a deep learning-based approach to assess the quality of input retinal images is proposed. The method begins with a deep learning-based classification that identifies the image quality in terms of sharpness, illumination, and homogeneity, followed by an unsupervised second level that evaluates the field definition and content of the image. The proposed method is general and robust, making it more suitable than the alternative methods currently adopted in clinical practice. Finally, an automatic deep learning-based method for clinically significant macular edema triage is proposed. The classified high-quality retinal images are used as inputs. Both full image and ARIA processed image are experimented as the possible inputs. Deep convolutional neural networks are used as feature extractors. The extracted features are over-sampled to balance the highly skewed database samples across the examined classes. Finally, using the reduced feature set obtained through feature selection, a simple k-NN classifier demonstrates significant classification performance, thereby validating the preliminary objective of this study.