Download or read book Retinal image analysis by mixture model based clustering and discriminant analysis for automatic detection of hard exudates and haemorrhages written by Clara Isabel Sánchez Gutiérrez and published by . This book was released on 2008 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Image Detection of Retinal Pathology written by Herbert Jelinek and published by CRC Press. This book was released on 2009-10-09 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discusses the Effect of Automated Assessment Programs on Health Care ProvisionDiabetes is approaching pandemic numbers, and as an associated complication, diabetic retinopathy is also on the rise. Much about the computer-based diagnosis of this intricate illness has been discovered and proven effective in research labs. But, unfortunately, many of

Download or read book Automated Retinal Image Analysis for Detection and Measurements of Tortuosity and Exudates written by and published by . This book was released on 2014 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last few decades, an automated retinal image analysis for a diabetic retinopathy has been a major area of attention in the computer vision. The typical approach used by Ophthalmologists for examining the eye is the pupil dilation. This takes time, is not accurate, and is uncomfortable for patients. On the other hand, the automated retinal image analysis for retina pathologies is more sophisticated technology by which Ophthalmologists could screen the retina of the eye regularly and find out its normal and abnormal structures in a more precise and comfortable way. Monitoring the retina of the eye, utilizing an automatic method, and by applying necessary cure in advance could save patients from losing their vision. In recent time, there were many research works on automated detection and classification of the features of the eye in the fundus [normal structures and abnormal structures (retina pathologies)] using different strategies and algorithms to obtain precise results. But they still do not meet many of the requirements. In this research we consider the retinal images taken from non-dilated eye pupils to eliminate the dilation process. These images are noisy, lower in contrast, lower in intensity, and have more non-uniform luminosity due to a non-dilation process and retinal camera. The contributions of this research are robust algorithms and methods that detect and extract as well as measure the landmark features of the retina such as the optic disc, and blood vessels as well as the abnormal structures such as blood vessel tortuosity, hard exudates and soft exudates (cotton wool spots), and an age-related macular degeneration (drusens). This provides early detection and monitoring of retina pathologies for a patient that can be cured by ophthalmologists prior to blindness. We investigated our developed algorithm by applying it to a number of retinal images with noise, low intensity, less color contrast, and non-uniform luminosity which are taken from non-dilated eye pupil. In addition to that, these images carry distinct kinds of retina pathologies such as exudates, drusens, and tortuosity.

Download or read book Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy written by Tomi Kauppi and published by . This book was released on 2010 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Analysis of Retinal Images written by Enrico Grisan and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Detection and Analysis of Retinal Diseases written by Hillol Das and published by LAP Lambert Academic Publishing. This book was released on 2014-10-09 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image processing and analysis has great significance in the field of medical science. One such area is the analysis of retinal fundus images. This book explains an automated system for detection and analysis of retinal diseases from fundus images. Common retinal diseases such as Diabetic Retinopathy, Retinitis Pigmentosa and Glaucoma are studied in detail and appropriate detection approaches has been discussed. These diseases usually causes vision loss that in many cases cannot be reversed in advanced stages whereas they can be controlled in the early stages otherwise it progresses to loss of central vision and leads to complete blindness. Experimental results shows very significant detection rate of the methods discussed especially in locating optic disk with normalized cross correlation function. The proposed automated detection system can therefore be deployed for mass screening of retinal fundus images for early detection of common retinal diseases in rural areas where there is non-availability of good number of ophthalmologist and reducing the screening cost in the same time.

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 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 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 Modeling Pattern Analysis and Feature based Retrieval on Retinal Images written by Huajun Ying and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Inexpensive high quality fundus camera systems enable imaging of retina for vision related health management and diagnosis at large scale. A computer based analysis system can help establish the general baseline of normal conditions vs. anomalous ones, so that different classes of retinal conditions can be classified. Advanced applications, ranging from disease screening algorithms, aging vs. disease trend modeling and prediction, and content-based retrieval systems can be developed. In this dissertation, I propose an analytical framework for the modeling of retina blood vessels to capture their statistical properties, so that based on these properties one can develop blood vessel mapping algorithms with self-optimized parameters. Then, other image objects can be registered based on vascular topology modeling techniques. On the basis of these low level analytical models and algorithms, the third major element of this dissertation is a high level population statistics application, in which texture classification of macular patterns is correlated with vessel structures, which can also be used for retinal image retrieval. The analytical models have been implemented and tested based on various image sources. Some of the algorithms have been used for clinical tests. The major contributions of this dissertation are summarized as follows: (1) A concise, accurate feature representation of retinal blood vessel on retinal images by proposing two feature descriptors Sp and Ep derived from radial contrast transform. (2) A new statistical model of lognormal distribution, which captures the underlying physical property of the levels of generations of the vascular network on retinal images. (3) Fast and accurate detection algorithms for retinal objects, which include retinal blood vessel, macular-fovea area and optic disc, and (4) A novel population statistics based modeling technique for correlation analysis of blood vessels and other image objects that only exhibit subtle texture changes.

Download or read book Retinal Image Analysis for Eye Disease Detection and Classification written by Mohamed Omar 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 Structure Analysis and Lesion Detection from Retinal Fundus Images written by Ana Guadalupe Salazar Gonzalez and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.

Download or read book Automated Analysis of Retinal Images for Detection of Age related Macular Degeneration and Diabetic Retinopathy written by Mark Johannes Josephus Petrus van Grinsven and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Digital Image Processing for Ophthalmology written by Faraz Oloumi and published by Morgan & Claypool Publishers. This book was released on 2014-05-01 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: The monitoring of the effects of retinopathy on the visual system can be assisted by analyzing the vascular architecture of the retina. This book presents methods based on Gabor filters to detect blood vessels in fundus images of the retina. Forty images of the retina from the Digital Retinal Images for Vessel Extraction (DRIVE) database were used to evaluate the performance of the methods. The results demonstrate high efficiency in the detection of blood vessels with an area under the receiver operating characteristic curve of 0.96. Monitoring the openness of the major temporal arcade (MTA) could facilitate improved diagnosis and optimized treatment of retinopathy. This book presents methods for the detection and modeling of the MTA, including the generalized Hough transform to detect parabolic forms. Results obtained with 40 images of the DRIVE database, compared with hand-drawn traces of the MTA, indicate a mean distance to the closest point of about 0.24mm. This book illustrates applications of the methods mentioned above for the analysis of the effects of proliferative diabetic retinopathy and retinopathy of prematurity on retinal vascular architecture.

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: