Download or read book Automatic Detection and Quantification of Blood Vessels in the Vicinity of the Optic Disc in Digital Retinal Images written by Wei Huang and published by . This book was released on 2006 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Automatic Segmentation Detection of Blood Vessels and Optic Disc in Retinal Images written by Anchal Sharma and published by Infinite Study. This book was released on with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conceptual Segmentation is a critical technique in medical imaging. The Processes of identification and division of optic circle and veins are the fundamental strides for the analysis of a few infections that causes visual deficiency like diabetic retinopathy, hypertension, glaucoma and different visual deficiency ailment.

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 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 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 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 Digital Image Processing for Ophthalmology written by Faraz Oloumi and published by Springer Nature. This book was released on 2022-06-01 with total page 151 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 RETINAL BLOOD VESSELS SEPARATION A SURVEY written by SINDHU SARANYA and published by Infinite Study. This book was released on with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Segmenting blood vessel from the retinal image is important for detecting many retinal vascular disorders. Diseases which are all affecting the blood vessels of the eye are known as Retinal vascular disorders.

Download or read book A Method of Disease Detection and Segmentation of Retinal Blood Vessels using Fuzzy C Means and Neutrosophic Approach written by Ishmeet Kaur and published by Infinite Study. This book was released on with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Diabetic Retinopathy is a disease which causes a menace to the eyesight. The detection of this at an early stage can aid the person from vision loss. The examination of retinal blood vessel structure can help to detect the disease, so segmentation of retinal blood vessel vasculature is important and is appreciated by the ophthalmologists

Download or read book Optic Disc Detection in Fluorescein Angiography Images written by Srinivasulu Avvaru and published by LAP Lambert Academic Publishing. This book was released on 2012-04 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optic disc detection is an important task in retinal imaging as its significance justifies whether the retina is normal or abnormal. The optic disc represents the entrance and exit sites of vascular and nervous structures, and its size and shape could be used in diagnostics and treatment of diseases, such as glaucoma. Several approaches have been proposed, the majority of which use intensity or shape based techniques. Recently, approaches that combine intensity, shape and information regarding vascular structures have been used with good results. In this paper, a method that combines information from the major blood vessels is investigated and compared with intensity and shape based techniques. The image that is employed to evaluate the proposed technique consists of both healthy and unhealthy retinas. The techniques used in this contribution result to a robust, fast and accurate technique for detection of the optic disc.

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 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 Automated Delineation and Quantitative Analysis of Blood Vessels in Retinal Fundus Image written by Xiayu Xu and published by . This book was released on 2012 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated fundus image analysis plays an important role in the computer aided diagnosis of ophthalmologic disorders. A lot of eye disorders, as well as cardiovascular disorders, are known to be related with retinal vasculature changes. Many studies has been done to explore these relationships. However, most of the studies are based on limited data obtained using manual or semi-automated methods due to the lack of automated techniques in the measurement and analysis of retinal vasculature. In this thesis, a fully automated retinal vessel width measurement technique is proposed. This novel method models the accurate vessel boundary delineation problem in two-dimension into an optimal surface segmentation problem in threedimension. Then the optimal surface segmentation problem is transformed into finding a minimum-cost closed set problem in a vertex-weighted geometric graph. The problem is modeled differently for straight vessel and for branch point because of the different conditions in straight vessel and in branch point. Furthermore, many of the retinal image analysis needs the location of the optic disc and fovea as a prerequisite information, for example, in the analysis of the relationship between vessel width and the distance to the optic disc. Hence, a simultaneous optic disc and fovea detection method is presented, which includes a two-step classification of three classes. The major contributions of this thesis include: 1) developing a fully automated vessel width measurement technique for retinal blood vessels, 2) developing a simultaneous optic disc and fovea detection method, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies.

Download or read book Retinal Image Segmentation and Quantification of Vessel Width in Non standard Retinal Datasets written by Muhammad Moazam Fraz and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The human retina has the potential to reveal important information about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. Automatic quantification of retinal vessel morphology and width is considered as a first step in computer assisted medical applications related to diagnosis and treatment planning. This work aims to quantify the blood vessels in noisy and pathological retinal images of school children with uneven illumination and containing complex vessel profiles. In this thesis, we have presented two methodologies of retinal vessel segmentation and an algorithm for vessel width measurement. The unsupervised method of retinal segmentation is based on detection of vessel centrelines and followed by computing the vessel shape and the orientation map using morphological bitplane slicing. A supervised method for segmentation of blood vessels by using an ensemble classifier of boosted and bagged decision trees is also presented. The feature vector encodes information to successfully handle both normal and pathological retinas with bright and dark lesions simultaneously. The obtained performance metrics illustrate that this method outperforms most of the state-of-the-art methodologies of retinal vessel segmentation. The method is computationally fast in training and classification and needs fewer samples for training than other supervised methods. It is training set robust as it offers a better performance even when it is trained and tested on different sets of retinal images. A new public database of the retinal images taken from multi-ethnic school children is presented along with the ground truths of vessel segmentation and width measurement. We have also introduced a robust and accurate methodology for measuring the calibre of vessel segments in retinal images of multi-ethnic children. The vessel centrelines are detected from the vessel probability map image resulting from ensemble classification. The vessel branch points and crossovers are identified and removed from the vessel centreline image to obtain vessel segments followed by computing the local vessel orientation of the vessel segments. The width of each vessel segment is estimated using a two dimensional model with incorporated Gaussian (for ordinary vessels) as well as Difference of Gaussian profiles (for vessels with a central reflex). The automated methods for quantification of retinal vessel morphology and width may be used as an alternative to the time consuming subjective clinical evaluation for monitoring the progression of retinopathies and their association with normal and abnormal vascular patterns. This may enable a quick diagnosis, treatment availability, prognosis, and facilitation of clinical heath-care procedures in remote areas.

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 A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images written by Yanhui Guo and published by Infinite Study. This book was released on with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image’s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.â

Download or read book Image Processing of Colour Retinal Images written by Lee Streeter and published by . This book was released on 2004 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: