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Aim. Optical Coherence Tomography (OCT) is a fast and non-invasive medical imaging technique which helps in the investigation of each individual retinal layer structure. For early detection of retinal diseases and the study of their progression, segmentation of the OCT images into the distinct layers of the retina plays a crucial role. However, segmentation done by the clinicians manually is extremely tedious, time-consuming and variable with respect to the expertise level. Hence, there is an utmost necessity to develop an automated segmentation algorithm for retinal OCT images which is fast, accurate, and eases clinical decision making. Methods. Graph-theoretical methods have been implemented to develop an automated segmentation algorithm for spectral domain OCT (SD-OCT) images of the retina. As a pre-processing step, the best method for denoising the SD-OCT images prior to graph-based segmentation was determined by comparison between simple Gaussian filtering and an advanced wavelet-based denoising technique. A shortest-path based graph search technique was implemented to accurately delineate intra-retinal layer boundaries within the SD-OCT images. The results from the automated algorithm were also validated by comparison with manual segmentation done by an expert clinician using a specially designed graphical user interface (GUI). Results. The algorithm delineated seven intra-retinal boundaries thereby segmenting six layers of the retina along with computing their thicknesses. The thickness results from the automated algorithm when compared to normative layer thickness values from a published study showed no significant differences (p > 0.05) for all layers except layer 4 (p = 0.04). Furthermore, when a comparative analysis was done between the results from the automated segmentation algorithm and that from manual segmentation by an expert, the accuracy of the algorithm varied between 74.58% (layer 2) to 98.90% (layer 5). Additionally, the comparison of two different denoising techniques revealed that there was no significant impact of an advanced wavelet-based denoising technique over the use of simple Gaussian filtering on the accuracy of boundary detection by the graph-based algorithm. Conclusion. An automated graph-based algorithm was developed and implemented in this thesis for the segmentation of seven intra-retinal boundaries and six layers in SD-OCT images which is as good as manual segmentation by an expert clinician. This thesis also concludes on the note that simple Gaussian filters are sufficient to denoise the images in graph-based segmentation techniques and does not require an advanced denoising technique. This makes the complexity of implementation far more simple and efficient in terms of time and memory requirements.
This paper presents a fully-automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema (DME).
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.
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.
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.
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.
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
This book provides a collection of optical coherence tomographic (OCT) images of various diseases of posterior and anterior segments. It covers the details and issues of diagnostic tests based on OCT findings which are crucial for ophthalmologists to understand in their clinical practice. Throughout the chapters all aspects of this non-invasive, popular imaging technique, known for ingenuity and accuracy, is clearly illustrated. Atlas of Ocular Optical Coherence Tomography has been categorized into eleven sections, discussing and illustrating distinct OCT features, as well as showing other image modalities such as fluorescein angiography, fundus autofluorescence, perimetry and laboratory examination. This book also covers choroidal pathologies and vitreous abnormalities. The last section has been allocated to anterior segment disease, including cornea, angle, iris and conjunctival abnormalities. Above all, the numerous images, and detailed descriptions of diseases, make this book an essential guide for general ophthalmologists and ophthalmology residences.