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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...
Image analysis is becoming increasingly prominent as a non intrusive diagnosis in modern ophthalmology. Blood vessel morphology is an important indicator for diseases like diabetes, hypertension and retinopathy. This paper presents an automated and unsupervised method for retinal blood vessels segmentation using the graph cut technique. The graph is constructed using a rough segmentation from a pre-processed...
Automated segmentation of blood vessels in retinal images will help eye care specialists screen larger populations for vessel abnormalities. However, automated retinal segmentation is complicated by the fact that a number of vessels are very thin and the local contrast is low. We propose the radial projection method to locate the vessel centerlines which contains the thin vessels. Then the aggregate...
In this paper, we present a method for cup boundary detection from monocular colour fundus image to help quantify cup changes. The method is based on anatomical evidence such as vessel bends at cup boundary, considered relevant by glaucoma experts. Vessels are modeled and detected in a curvature space to better handle inter-image variations. Bends in a vessel are robustly detected using a region of...
Glaucoma is a disease characterized by elevated intraocular pressure (IOP). This increased IOP leads to damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. CDR is a key indicator for the detection of glaucoma. The existing approaches determined the CDR using manual threshold analysis which is fairly time consuming. This paper proposes two methods to extract the...
Retinal vessel segmentation is important for the detection of numerous eye diseases and plays an important role in automatic retinal screening systems. K-nearest neighbor classifier is used to perform a soft segmentation of retinal vessels and is a supervised method. This method produces segmentation by classifying each image pixel as vessel or nonvessel, based on the output of filters and the pixel...
This paper presents an efficient method for automatic extraction of blood vessels in retinal images to improve the detection of low contrast and narrow vessels. The proposed algorithm is composed of four steps: curvelet-based contrast enhancement, match filtering, curvelet-based edge extraction, and length filtering. In this base, after reconstruction of enhanced image from the modified curvelet coefficients,...
Efficient optic disk (OD) localization and segmentation are important tasks in automated retinal screening. In this paper, we take digital curvelet transform (DCUT) of the enhanced retinal image and modify its coefficients based on the sparsity of curvelet coefficients to get probable location of OD. If there are not yellowish objects in retinal images or their size are negligible, we can then directly...
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...
In this paper, we present a method to automatically extract the vessel segments and construct the vascular tree with anatomical realism from a color retinal image. The significance of the work is to assist in clinical studies of diagnosis of cardio-vascular diseases, such as hypertension,which manifest abnormalities in either venous and/or arterial vascular systems. To maximize the completeness of...
Retinal vessel segmentation is an essential step for the diagnoses of various eye diseases. An automated tool for blood vessel segmentation is useful to eye specialists for purpose of patient screening and clinical study. Vascular pattern is normally not visible in retinal images. In this paper, we present a method for enhancing, locating and segmenting blood vessels in images of retina. We present...
We present an automatic method to segment the blood vessels in retinal images. Our method is based on tracking the center of the vessels using the Kalman filter. We define a linear model to track the blood vessels, suitable for both the detection of wide and thin vessels in noisy images. The estimation of the next state is computed by using gradient information, histogram of the orientations and the...
In this paper, a novel vesselness measure based on analysis of the Hessian matrix is presented. The larger eigenvalue of the Hessian matrix is used for vessel centerlines detection, while vessel orientations are estimated from the eigenvectors corresponding to the smaller eigenvalue. The vesselness measure combines information from vessel centerlines and orientations over scales to segment retinal...
In automated diagnosis of diabetic retinopathy, retinal images are used. The retinal images of poor quality need to be enhanced before the extraction of features and abnormalities. Segmentation of retinal images is essential for this purpose. The segmentation is employed to smooth and strengthen images by separating the noisy area from the overall image thus resulting in retinal image enhancement...
This paper presents a novel method for identification of the position of the optic disc in retinal images. The method is based on the preliminary detection of the main edge detection of retinal image. The segmentation optic disc is estimated as a circular area. We searched for areas of optic disc using Hough transform which detected several straight lines and approximated them as a circular line....
Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in colour fundus images. Matched filtering is utilized to...
In this paper, a novel approach for vessels extraction edge-based image segmentation is proposed. Vessels segmentation and extraction play an important role in supporting computer assistance for diagnosis of Diabetic Retinopathy (DR). Diabetic Retinopathy is a severe and widely spread eye disease. The algorithms to detect and extract vessels from retinal images are mainly based on morphological filtering...
In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non-...
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...
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