Download or read book Symbiotic Registration and Deep Learning for Retinal Image Analysis written by Li Ding (Electrical and computer engineering researcher) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Geometry and semantics are two sub-fields in computer vision that have been researched extensively as two separate problems over decades. However, semantics and geometry in computer vision are not mutually exclusive and techniques developed for one could complement the other. Unfortunately, the interplay of these two fields has received limited attention. In this thesis, we design symbiotic geometric and semantic computer vision methods in the specific context of retinal image analysis, where we consider the semantic problem of retinal vessel detection and the geometric problem of retinal image registration. First, we propose a novel pipeline for vessel detection in fluorescein angiography (FA) using deep neural networks (DNNs) that reduces the effort required for labeling ground truth data by combining cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured color fundus (CF) and fundus FA. Binary vessels maps detected from CF images with a pre-trained network are geometrically registered with FA images via robust parametric chamfer alignment. Using the transferred vessels as initial ground truth labels, the human-in-the-loop approach progressively improves the ground truth labeling by iterating between deep-learning and labeling. Experiments show that the proposed pipeline significantly reduces the annotation effort and outperforms prior FA vessel detection methods by a significant margin. Next, we describe an annotation-efficient deep learning framework for vessel detection in UWF fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach uses concurrently captured UWF FA and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, UWF FA vessel maps detected with a pre-trained DNN are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps are used as the tentative training data but inevitably contain incorrect labels due to the differences between the two modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The registration and the vessel detection benefit from each other and are progressively improved. Results on two datasets show that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data. Finally, we present a hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in UWF FA. Our approach consists of a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies corresponding keypoints and estimates a polynomial geometric transformation consistent with the identified correspondences between reference and target images. Our RANSAC modification particularly improves feature matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The local refinement is formulated as a parametric chamfer alignment for vessel maps obtained using DNNs. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. Experiments conducted on two datasets show that the proposed framework significantly outperforms the existing retinal image registration methods"--Pages xv-xvii.

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 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 Retinal Image Analysis Based on Deep Learning written by B. Q. Al-Bander and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Registration and Quantitative Analysis of Retinal Images and Sequences written by Adrià Pérez Rovira and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Symbiotic Deep Learning for Medical Image Analysis with Applications in Real time Diagnosis for Fetal Ultrasound Screening written by Samuel Budd and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi Modal Retinal Image Registration Via Deep Neural Networks written by Junkang Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-modal retinal images provide complementary anatomical information at various resolutions, color wavelengths, and fields of view. Aligning multi-modal images will establish a comprehensive view of the retina and benefit the screening and diagnosis of eye diseases. However, the inconsistent anatomical patterns across modalities create outliers in feature matching, and the lack of retinal boundaries may also fool the intensity-based alignment metrics, both of which will influence the alignment qualities. Besides, the varying distortion levels across Ultra-Widefield (UWF) and Narrow-Angle (NA) images, due to different camera parameters, will cause large alignment errors in global transformation. In addressing the issue of inconsistent patterns, we use retinal vasculature as a common signal for alignment. First, we build a two-step coarse-to-fine registration pipeline fully based on deep neural networks. The coarse alignment step estimates a global transformation via vessel segmentation, feature detection and description, and outlier rejection. While the fine alignment step corrects the remaining misalignment through deformable registration. In addition, we propose an unsupervised learning scheme based on style transfer to jointly train the networks for vessel segmentation and deformable registration. Finally, we also introduce Monogenical Phase signal as an alternative guidance in training the deformable registration network. Then, to deal with the issue of various distortion levels across UWF and NA modalities, we propose a distortion correction function to create images with similar distortion levels. Based on the assumptions of spherical eyeball shape and fixed UWF camera pose, the function reprojects the UWF pixels by an estimated correction camera with similar parameters as the NA camera. Besides, we incorporate the function into the coarse alignment networks which will simultaneously optimize the correction camera pose and refine the global alignment results. Moreover, to further reduce misalignment from the UWF-to-NA global registration, we estimate a 3D dense scene for the UWF pixels to represent a more flexible eyeball shape. Both the scene and the NA camera parameters are iteratively optimized to reduce the alignment error between the 3D-to-2D reprojected images and the original ones, which is also concatenated with the coarse alignment networks with distortion correction function.

Download or read book Computer Registration and Processing of Retinal Images written by John Roger Jagoe and published by . This book was released on 1995 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Equivariance For Deep Learning And Retinal Imaging written by Daniel Ernest Worrall and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysing and generalizing from small datasets is still a difficult challenge. However, the de facto model in computer vision, the convolutional neural network (CNN), is known to be data hungry and poorly interpretable. We investigate a new design principle called equivariance, which we demonstrate can both improve data efficiency and interpretability in CNNs. Intermediate layers of an equivariant CNN preserve information about local and global transformations of the input image. Moreover, we build specific equivariances into a CNN, such that they need not be learned from data. As a result, less data is needed, improving the network generalization. In this thesis, we explore three techniques to demonstrate the usefulness of equivariance. We start by considering equivariance to 3D right-angle rotations in the context of voxelized data. We show that this improves classification performance on the ModelNet10 dataset, among non-ensembled models. We then introduce Harmonic Networks, which are equivariant to continuous rotations in 2D. These exploit the fact that the Fourier transform naturally disentangles transformation and magnitude information. To the best of our knowledge, this is the first CNN architecture exhibiting equivariance to a continuous transformation. Lastly we consider learning equivariance from data, to model equivariances, which may not be tractably designed into a network. To validate our work, we harness a real-world case study by applying some of our developments to the difficult challenge of Retinopathy of Prematurity (ROP) detection. ROP is a sight-threatening disease, affecting 60% of premature babies below 32 weeks post-menstrual age and 1500g in weight. ROP is difficult to detect, its symptomatology is not fully understood, and doctors all over the world are increasingly burdened with screening, as the number of at-risk neonates sharply increases. To the best of our knowledge, we are the first to develop a deep learning solution to ROP detection.

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.

Download or read book Feature based Retinal Image Registration written by Chia-Ling Tsai and published by . This book was released on 2003 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Retinal Image Registration by Mutual Information Maximization written by Xiao-Hong Zhu and published by . This book was released on 2001 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Retinal Image Analysis and Its Use in Medical Applications written by Yibo Zhang and published by . This book was released on 2011 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Retina located in the back of the eye is not only a vital part of human sight, but also contains valuable information that can be used in biometric security applications, or for the diagnosis of certain diseases. In order to analyze this information from retinal images, its features of blood vessels, microaneurysms and the optic disc require extraction and detection respectively. We propose a method to extract vessels called MF-FDOG. MF-FDOG consists of using two filters, Matched Filter (MF) and the first-order derivative of Gaussian (FDOG). The vessel map is extracted by applying a threshold to the response of MF, which is adaptively adjusted by the mean response of FDOG. This method allows us to better distinguish vessel objects from non-vessel objects. Microaneurysm (MA) detection is accomplished with two proposed algorithms, Multi-scale Correlation Filtering (MSCF) and Dictionary Learning (DL) with Sparse Representation Classifier (SRC). MSCF is hierarchical in nature, consisting of two levels: coarse level microaneurysm candidate detection and fine level true microaneurysm detection. In the first level, all possible microaneurysm candidates are found while the second level extracts features from each candidate and compares them to a discrimination table for decision (MA or non-MA). In Dictionary Learning with Sparse Representation Classifier, MA and non-MA objects are extracted from images and used to learn two dictionaries, MA and non-MA. Sparse Representation Classifier is then applied to each MA candidate object detected beforehand, using the two dictionaries to determine class membership. The detection result is further improved by adding a class discrimination term into the Dictionary Learning model. This approach is known as Centralized Dictionary Learning (CDL) with Sparse Representation Classifier. The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians, which are larger and have thicker vessels compared to Caucasians. We propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The proposed extraction/detection approaches are tested in medical applications, specifically the case study of detecting diabetic retinopathy (DR). DR is a complication of diabetes that damages the retina and can lead to blindness. There are four stages of DR and is a leading cause of sight loss in industrialized nations. Using MF-FDOG, blood vessels were extracted from DR images, while DR images fed into MSCF and Dictionary and Centralized Dictionary Learning with Sparse Representation Classifier produced good microaneurysm detection results. Using a new database consisting of only Asian DR patients, we successfully tested our OD detection method. As part of future work we intend to improve existing methods such as enhancing low contrast microaneurysms and better scale selection. In additional, we will extract other features from the retina, develop a generalized OD detection method, apply Dictionary Learning with Sparse Representation Classifier to vessel extraction, and use the new image database to carry out more experiments in medical applications.

Download or read book A Novel Deep Learning based Framework for Context Aware Semantic Segmentation in Medical Imaging written by Muhammad Zubair Khan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. Experts believe that intelligent applications designed for medicine will soon take over the role of a radiologist. The goal of bringing semantic segmentation, specifically in healthcare, is to boost efficiency in diagnostics by labeling every pixel with its corresponding class. The core concept revolved around taking random input size and produce output with similar size and sufficient inference. Over time, researchers have proposed distinct architectures for end-to-end and pixel-to-pixel self-sufficient training. In retinal image analysis, the development of semantic segmentation techniques has opened doors for researchers to precisely extract regions of interest and automatically detect the symptoms of various retinal diseases such as diabetic and hypertensive retinopathy. These diseases are common in subjects with diabetes and hypertension. It may cause vascular occlusion and produce fragile micro-vessels in an advanced stage of neovascularization. An excessive amount of sugar in the blood and extended force on vessels rupture the newly developed micro-vessels, providing blood and other fluids leak into the retina. It may cause blindness in severe cases if not diagnosed and treated at an early stage. It is critical to find the initial symptoms, including abnormal vascular growth, hard exudates, arterial and venous occlusion, and the appearance of bulges on the outer layer of vessels. The primary step is to automate the analysis of variations, appear in vessels for detecting retinopathy. In our dissertation, we have performed a context-sensitive semantic segmentation to capture long-range dependencies and restore lossy pixels of manually annotated groundtruths. We also applied morphological image processing techniques to create masks for unmasked datasets. The method adopted literature to apply leave-one-out and k-fold strategies for unordered data distributions. The use of context information in predicting target pixels bring added precision to the vision-critical system. We also designed a fully automated screening system based on a unified modeling approach of diagnosis. The system can extract multiple ocular features with a novel semantic segmentation network to early detect the symptoms of retinal disease. We proposed a novel technique of dynamic inductive learning with single-point decision criteria, striving to optimize the image segmentation model using multi-criteria decision support feedback. It is found that dynamic inductive transfer learning reduces the subjectivity of hyperparameter selection in a model validation process. We further designed a feature-oriented ensemble network for extracting multiple retinal features. It includes a set of models that reflect feature-based needs to prevent intensity loss, micro-vessels overlap, and data redundancy. The proposed learning protocol with a minimalist approach can compete with state-of-the-art work without a performance compromise. The pitfall of previously proposed work is also addressed through the self-defined assessment criteria. Our research also proposed an architecture inspired by the generative adversarial network. The network enhanced the base model with residual, dense, and attention mechanisms. The residual mechanism helped extend the network depth without falling into the gradient problem and improved the model response by transmitting useful feature representations. The dense block helped increase the information flow for reusing feature representations that reduced the number of trainable parameters. However, the attention mechanism performed the domain-centric synthesis and helped conserve local context by highlighting fine details. Our model shown a promising response in extracting both macro and micro-vessels and reported high true positive rate and structural similarity index scores.

Download or read book Analysis and Optimization of Machine Learning Based Medical Image Registration Framework written by Sergey Sergeev and published by . This book was released on 2013 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi modal Image Registration with Unsupervised Deep Learning written by Courtney K. Guo and published by . This book was released on 2019 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we tackle learning-based multi-modal image registration. Multi-modal registration, in which two images of dierent modalities need to be aligned to each other, is a difficult yet essential task for medical imaging analysis. Classical methods have been developed for single-modal and multi-modal registration, but are slow because they solve an optimization problem for each pair of images. Recently, deep learning methods for registration have been proposed, and have been shown to shorten registration time by learning a global function to perform registration, which can then be applied quickly on a pair of test images. These methods perform well for single-modal registration but have not yet been extended to the harder task of multi-modal registration. We bridge this gap by implementing classical multi-modal metrics in a differentiable and efficient manner to enable deep image registration for multi-modal data. We nd that our method for multi-modal registration performs significantly better than baselines, in terms of both accuracy and runtime.