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Fundus Fluorescein Angiography (FA) is a powerful tool for imaging and evaluating Diabetic Macular Edema (DME), where the fluorescein dye leaks and accumulates in the diseased areas. Currently, the assessment of FA images is qualitative and suffers from large inter-observer variability. A necessary step towards quantitative assessment of DME is automatic segmentation of fluorescein leakage. In this...
A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this...
Abnormalities in the retinal vessel tree are associated with different pathologies. Usually, they affect arteries and veins differently. In this regard, the arteriovenous ratio(AVR) is a measure of retinal vessel caliber, widely used in medicine to study the influence of these irregularities in disease evolution. Hence, the development of an automatic tool for AVR computation as well as any other...
In this paper, a computer approach is proposed for recognition of retina layers on optical coherence tomography (OCT) images. OCT uses the optical backscattering of light to scan the eye and describe a pixel representation of the anatomic layers within the retina. Our approach is based on co-occurrence matrix for feature extraction and a supervised learning method for classification, which four features...
Fluorescein angiograms of the human retina are widely used in the diagnosis and treatment of several diseases such as diabetic retinopathy and age relate macular degeneration. They analyze the micro circulation of the retina and choroid. Hence, accurate extraction of the vascular tree network is of crucial importance. Previous approaches to retinal vessel extraction assume either bright vessels on...
Glaucoma is the second leading cause of blindness worldwide. The risk of glaucoma can be determined by calculating the cup to disc ratio in retinal fundus images. To accurately detect the optic cup, kinks or bends in small and medium vessels are important indicators of the cup boundary. In this paper, we present a method of detecting such vessels, through the extraction of patches and generation of...
In this paper, we propose a classification mechanism for retinal images so that the retinal images can be successfully distinguished from nonretinal images, egg yolk images for example. The proposed classification mechanism consists of two procedures: training and classification. The image features of retinal images and nonretinal images are extracted at the beginning of the training procedure to...
This paper presents an efficient approach for automatic detection of red lesions in ocular fundus images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of red lesions against the background. The enhanced red lesions are then segmented...
Manual segmentation of retinal blood vessels in optic fundus images is a tiresome task. Several methods have previously been proposed for the automatic segmentation of retinal blood vessels. In this paper we propose a classifier-based method. First the images are preprocessed so that the within class variability of the vessel and background classes are minimized. Next, the image is scanned with a...
We present the application of an Amplitude-Modulation Frequency-Modulation (AM-FM) method for extracting potentially relevant features towards the classification of diseased retinas from healthy retinas. In terms of AM-FM features, we use histograms of the instantaneous amplitude, the angle of the instantaneous frequency and the magnitude of the instantaneous frequency extracted over different frequency...
A method is proposed for the representation of localised features using disjoint sub-images taken from several datasets of retinal images for use within an incremental learning system. A tile-based localised adaptive threshold selection method was taken for vessel segmentation based on separate colour components. Arteriole-venous differentiation was done using the composite of these components and...
This paper proposes a new method for automatic segmentation of the vasculature in retinal images. The method is based on the analysis of feature vectors extracted from a prototype image, to classify pixels as vessel or non-vessel, using a multilayer feed forward neural network. The feature vectors are composed of the pixelspsila intensity and a continuous two-dimensional Morlet wavelet transform of...
Diabetic Retinopathy (DR) is a common cause of visual impairment among people of working age in industrialized countries. Automatic recognition of DR lesions, like hard exudates (HEs), in fundus images can contribute to the diagnosis and screening of this disease. In this study, we extracted a set of features from image regions and selected the subset which best discriminates between HEs and the retinal...
Due to its blood microcirculation, the retina is one of the first organs affected by hypertension and diabetes: retinal damages can lead to serious visual loss, that can be avoided by an early diagnosis. The most distinctive sign of diabetic retinopathy or severe hypertensive retinopathy are dark lesions such as haemorrhages and microaneurysms (HM), and bright lesions such as hard exudates (HE) and...
With the rapid advances in computing and electronic imaging technology, there has been increasing interest in developing computer aided medical diagnosis systems to improve the medical service for the public. Images of ocular fundus provide crucial observable features for diagnosing many kinds of pathologies such as diabetes, hypertension, and arteriosclerosis. A computer-aided retinal image analysis...
Detection and analysis of changes from retinal images is important in clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and in medical research. This paper presents a fully-automated approach for robust detection and classification of changes in longitudinal time-series of fluorescein angiograms (FA). The changes of interest here are related to the development...
Detection and analysis of changes from retinal images is important in clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and in medical research. This paper presents a fully-automated approach for robust detection and classification of changes in longitudinal time-series of fluorescein angiograms (FA). The changes of interest here are related to the development...
In this paper, a new feature vector for each pixel, in conjunction with the K-nearest neighbour classifier, is proposed for the segmentation of retinal blood vessels in digital colour fundus images. The proposed feature vector consists of two scale-space features - the largest eigenvalue and the gradient magnitude - of the intensity image, representing the two attributes of any vessel, i.e. the piecewise...
2D projection imaging is a widely used procedure for vessel visualization. For the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length or the number of vessel segments are needed. To achieve these goals vessel enhancement and segmentation are required. While there are already many vasculature specific vessel segmentation algorithms, we describe in this contribution...
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