Download or read book A Multimodal Machine learning Graph based Approach for Segmenting Glaucomatous Optic Nerve Head Structures from SD OCT Volumes and Fundus Photographs written by Mohammad Saleh Miri and published by . This book was released on 2016 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thus, the major contributions of this work include: 1) use of complementary information from SD-OCT and fundus images for segmenting the optic disc and cup boundaries in both modalities, 2) identifying the extent that accounting for the presence of externally oblique border tissue and retinal vessels in rim-width-based parameters affects structure-structure correlations, 3) designing a feature-based registration approach for registering multimodal images of the retina, and 4) developing a multimodal graph-based approach to segment the optic nerve head (ONH) structures such as Internal Limiting Membrane (ILM) surface and Bruch's membrane surface's opening.
Download or read book Multimodal 3 D Segmentation of Optic Nerve Head Structures from Spectral Domain Oct Volumes and Color Fundus Photographs written by Zhihong Hu and published by . This book was released on 2011 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: The obtained vessel positional information is then used to help enhance the NCO segmentation by incorporating that to the cost function of graph search. Note that the 3-D wavelet transform via lifting scheme has been used to remove high frequency noises and extract texture properties in SD-OCT volumes etc. The graph search has been used for finding the optimal solution of 3-D multiple surfaces using edge and additionally regional information. In this work, the use of the 3-D wavelet-transform-learning-based cost function for the graph search is a further extension of the 3-D wavelet transform and graph search. The major contributions of this work include: 1) extending the 3-D graph theoretic segmentation to the use of 3-D scale-learning-based cost function, 2) developing a graph theoretic approach for segmenting the NCO in SD-OCT volumes, 3) developing a 3-D wavelet-transform-learning-based graph theoretic approach for segmenting the NCO in SD-OCT volumes by iteratively utilizing the pre-identified NCO and vessel positional information (from 4 or 5), 4) developing a vessel classification approach in SD-OCT volumes by incorporating the pre-segmented NCO positional information to the vessel classification to suppress the NCO false positives, and 5) developing a multimodal concurrent classification and a registered-fundus approach for better identifying vessels in SD-OCT volumes using additional fundus information.
Download or read book Advances in Ocular Imaging in Glaucoma written by Rohit Varma and published by Springer Nature. This book was released on 2020-07-24 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serving as a practical guide to the ocular imaging modalities that are currently available to eye care providers for the care of glaucoma patients, this book provides information on advances in ocular imaging and their applications in the diagnosis and management of glaucoma. Each chapter introduces the imaging modality, highlight its strengths and weaknesses for clinical care, and discuss its integration into the clinical examination and decision-making process. The chapters also provide an in-depth description of the interpretation of images from each imaging modality. When appropriate, the chapters will summarize past and ongoing research and propose future research directions and clinical applications. This title will appeal to ophthalmologists and optometrists at all levels, from trainees to experienced clinicians looking to learn new and important information.
Download or read book A Combined Machine learning and Graph based Framework for the 3 D Automated Segmentation of Retinal Structures in SD OCT Images written by Bhavna Josephine Antony and published by . This book was released on 2013 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: A machine-learning based approach was also used here to learn descriptive features of the NCO. Thus, the major contributions of this work include 1) a method for the automated correction of axial artifacts in SD-OCT images, 2) a combined machine-learning and graph-theoretic framework for the segmentation of retinal surfaces in SD-OCT images (applied to humans, mice and canines), 3) a novel formulation of the graph-theoretic approach for the segmentation of multiple surfaces and their shared hole (applied to the segmentation of the neural canal opening), and 4) the investigation of textural markers that could precede structural and functional change in degenerative retinal diseases.
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 Automated 3 D Segmentation of Intraretinal Surfaces from Optical Coherence Tomography Images Centered on the Optic Nerve Head written by Bhavna Josephine Antony and published by . This book was released on 2009 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of retinal diseases. These high-resolution images of the retina allow structural changes to be detected and tracked. For instance, in glaucoma, the retinal nerve fiber layer (RNFL) has been known to thin. The recent availability of the considerably larger volumetric data from the spectral-domain OCT scanners has further increased the need for new processing techniques. This body of work is centered around an automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected through the computation of a minimum-cost closed set in a vertex-weighted graph constructed using edge/regional information, and subject to a priori determined varying surface interaction and smoothness constraints. The method also addresses the challenges posed by presence of the neural canal and the large blood vessels found at the ONH. The method was used to study RNFL thickness maps of normal and glaucomatous eyes, which showed average thicknesses of 73.72 +/- 32.72um and 60.38 +/- 25.22um (p
Download or read book Fully automated segmentation of fluid regions in exudative age related macular degeneration subjects Kernel graph cut in neutrosophic domain written by Abdolreza Rashno and published by Infinite Study. This book was released on with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain.
Download or read book A Novel Automatic Optic Disc and Cup Image Segmentation System for Diagnosing Glaucoma Using RIGA Dataset written by Ahmed Almazroa and published by . This book was released on 2016 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: The optic nerve head (ONH) of the retina is a very important landmark of the fundus and is altered in optic nerve pathology especially glaucoma. Numerous imaging systems are available to capture the retinal fundus and from which some structural parameters can be inferred the retinal fundus camera is one of the most important tools used for this purpose. Currently, the ONH structure examination of the fundus images is conducted by the professionals only by observation. It should be noted that there is a shortage of highly trained professional worldwide. Therefore a reliable and efficient optic disc and cup localization and segmentation algorithms are important for automatic eye disease screening and also for monitoring the progression/remission of the disease Thus in order to develop a system, a retinal fundus image dataset is necessary to train and test the new software systems. The methods for diagnosing glaucoma are reviewed in the first chapter. Various datasets of retinal fundus images that are publically available currently are described and discussed. In the second chapter the techniques for the optic disc and cup segmentations available in the literature is reviewed. While in the third chapter a unique retinal fundus image dataset, called RIGA (retinal images for glaucoma analysis) is presented. In the dataset, the optic disc and cup boundaries are annotated manually by 6 ophthalmologists (glaucoma professionals) independently for total of 4500 images in order to obtain a comprehensive view point as well as to see the variation and agreement between these professionals. Based upon these evaluations, some of the images were filtered based on a statistical analysis in order to increase the reliability. The new optic disc and cup segmentation methodologies are discussed in the fourth chapter. The process starts with a preprocessing step based on a reliable and precise algorithm. Here an Interval Type-II fuzzy entropy based thresholding scheme along with Differential Evolution was applied to determine the location of the optic disc in order to determine the region of interest instead of dealing with the entire image. Then, the processing step is discussed. Two algorithms were applied: one for optic disc segmentation based on an active contour model implemented by level set approach, and the second for optic cup segmentation. For this thresholding was applied to localize the disc. The disc and cup area and centroid are then calculated in order to evaluate them based on the manual annotations of areas and centroid for the filtered images based on the statistical analysis. In the fifth chapter, after segmenting the disc and cup, the clinical parameters in diagnosis of glaucoma such as horizontal and vertical cup to disc ratio (HCDR) and (VCDR) are computed automatically as a post processing step in order to compare the results with the six ophthalmologist's manual annotations results. The thesis is concluded in chapter six with discussion of future plans.
Download or read book Digital Image Processing for Ophthalmology written by Xiaolu Zhu and published by Springer Nature. This book was released on 2022-05-31 with total page 95 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 High Resolution Imaging in Microscopy and Ophthalmology written by Josef F. Bille and published by Springer. This book was released on 2019-08-13 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography written by FREERK G. VENHUIZEN and published by Infinite Study. This book was released on with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition.
Download or read book Estimation of Volumetric Optical Coherence Tomography Measurements from 2D Color Fundus Photographs Using Machine Learning written by Samuel Steven Johnson and published by . This book was released on 2019 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of the recent success of deep-learning methods in imaging domains, a convolutional neural network was also trained using the same data as was used with the random forest classifier. Because training data is used to help fine tune model parameters for deep learning, a subset of twelve randomly selected patients was strictly withheld from the training process to be used for testing. Comparing the prediction results on the withheld data with the OCT ground truth, we achieved a root-mean-square (RMS) error of 2.07 mm3 for the entire ONH region. Regional volumes for the nasal, temporal, inferior, superior, and peripapillary regions had RMS errors of 0.75 mm3, 0.82 mm3, 0.85 mm3, 0.91 mm3, and 1.62 mm3, respectively. Although the errors are slightly higher than those from the random forest model, the test dataset was smaller as we could not use a leave-patient-out validation approach and this may not be representative of the whole dataset since results were not averaged as before. It is also known that deep learning models require larger training datasets to achieve similar results to traditional machine-learning methods. For these reasons, and the fact that the errors were close to those of traditional methods, we believe deep learning approaches for estimating local retinal thickness in cases of optic disc swelling still holds promise with larger datasets. Both of the proposed approaches allow for clinicians to assess optic nerve edema in both a qualitative and quantitative manner using strictly fundus photography. The predictions allow for overall optic nerve head volume to be calculated as well as regional and local volumes which was not possible before.
Download or read book Deep Learning Based Multimodal Retinal Image Processing written by Yiqian Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The retina, the light sensitive tissue lining the interior of the eye, is the only part of the central nervous system (CNS) that can be imaged at micron resolution in vivo. Retinal diseases including age-related macular degeneration, diabetes retinopathy, and vascular occlusions are important causes of vision loss and have systemic implications for millions of patients. Retinal imaging is of great significance to diagnosing and monitoring both retinal diseases and systematic diseases that manifest in the retina. A variety of imaging devices have been developed, including color fundus (CF) photography, infrared reflectance (IR), fundus autofluorescence (FAF), dye-based angiography, optical coherence tomography (OCT), and OCT angiography (OCT-A). Each imaging modality is particularly useful for observing certain aspects of the retina, and can be utilized for visualization of specific diseases. In this dissertation, we propose deep learning based methods for retinal image processing, including multimodal retinal image registration, OCT motion correction, and OCT retinal layer segmentation. We present our established work on a deep learning framework for multimodal retinal image registration, a comprehensive study of the correlation between subjective and objective evaluation metrics for multimodal retinal image registration, convolutional neural networks for correction of axial and coronal motion artifacts in 3D OCT volumes, and joint motion correction and 3D OCT layer segmentation network. The dissertation not only proposes novel approaches in image processing, enhances the observation of retinal diseases, but will also provide insights on observing systematic diseases through the retina, including diabetes, cardiovascular disease, and preclinical Alzheimer's Disease. The proposed deep learning based retinal image processing approaches would build a connection between ophthalmology and image processing literature, and the findings may provide a good insight for researchers who investigate retinal image registration, retinal image segmentation and retinal disease detection.
Download or read book Multiple Surface Segmentation Using Novel Deep Learning and Graph Based Methods written by Abhay Shah and published by . This book was released on 2017 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: To accomplish the objectives of this thesis work, a comprehensive framework of graph based and deep learning methods is proposed to achieve the goal by successfully fulfilling the follwoing three aims. First, an efficient, automated and accurate graph based method is developed to segment surfaces which have steep change in surface profiles and abrupt distance changes between two adjacent surfaces. The developed method is applied and validated on intra-retinal layer segmentation of Spectral Domain Optical Coherence Tomograph (SD-OCT) images of eye with Glaucoma, Age Related Macular Degneration and Pigment Epithelium Detachment. Second, a globally optimal graph based method is developed to attain subvoxel and super resolution accuracy for multiple surface segmentation problem while imposing convex constraints. The developed method was applied to layer segmentation of SD-OCT images of normal eye and vessel walls in Intravascular Ultrasound (IVUS) images. Third, a deep learning based multiple surface segmentation is developed which is more generic, computaionally effieient and eliminates the requirement of human expert interventions (like transformation designs, feature extrraction, parameter tuning, constraint modelling etc.) required by existing surface segmentation methods in varying capacities. The developed method was applied to SD-OCT images of normal and diseased eyes, to validate the superior segmentaion performance, computation efficieny and the generic nature of the framework, compared to the state-of-the-art graph search method.
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 Fundus Autofluorescence written by Noemi Lois and published by Lippincott Williams & Wilkins. This book was released on 2012-02-13 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Featuring over 250 illustrations, this detailed full-color textbook provides up-to-date information on the use of fundus autofluorescence imaging in evaluation of retinal disease. Chapters describe the techniques available to image and quantify fundus autofluorescence and the autofluorescence patterns observed in the healthy eye and in various retinal diseases. Emphasis is on the value of fundus autofluorescence as a diagnostic and prognostic tool and its clinical utility in the context of other imaging techniques, such as fluorescein and indocyanine green angiography and optical coherence tomography. Each chapter also discusses the value of fundus autofluorescence in understanding the pathogenesis of the condition, and provides a comprehensive update on all aspects of the condition. A companion Website will offer the fully searchable text and an image bank.
Download or read book OCT and Imaging in Central Nervous System Diseases written by and published by . This book was released on 2020 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of OCT and Imaging in Central Nervous System Diseases offers updated state-of-the-art advances using optical coherence tomography (OCT) regrading neuronal loss within the retina. Detailed information on the OCT imaging and interpretation is provided for the evaluation of disease progression in numerous neurodegenerative disorders and as a biological marker of neuroaxonal injury. Covering disorders like multiple sclerosis, Parkinson's disease, Alzheimer's disease, intracranial hypertension, Friedreich's ataxia, schizophrenia, hereditary optic neuropathies, glaucoma, and amblyopia, readers will given insights into effects on the retina and the and optic nerve. Individual chapters are also devoted to OCT technique, new OCT technology in neuro-ophthalmology, OCT and pharmacological treatment, and the use of OCT in animal models. Similar to the first edition, this book is an excellent and richly illustrated reference for diagnosis of many retinal diseases and monitoring of surgical and medical treatment. OCT allows to study vision from of the retina to the optic tracts. Retinal axons in the retinal nerve fiber layer (RNFL) are non-myelinated until they penetrate the lamina cribrosa. Hence, the RNFL is an ideal structure for visualization of any process of neurodegeneration, neuroprotection, or regeneration. By documenting the ability of OCT to provide key information on CNS diseases, this book illustrates convincingly that the eye is indeed the "window to the brain".
