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The ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases, such as diabetes and hypertension. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. The extraction of blood vessels from retinal images is an important and challenging...
Clinical research suggests that changes in the retinal blood vessels (e.g., vessel caliber) are important indicators for earlier diagnosis of diabetes and cardiovascular diseases. Reliable vessel detection or segmentation is a prerequisite for quantifiable retinal blood vessel analysis for predicting these diseases. However, the segmentation of blood vessels is complicated by its huge variations such...
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...
Medical images enhanced at different scales have shown to give good results in mammogram enhancement, tumor classification, and lung nodule detection. However, the studies of multiscale analysis on retinal vasculature have been primarily limited to vessel segmentation. We propose using the Fourier Fractal dimension (FFD) to extract the complexity of the retinal vasculature enhanced at different wavelet...
A novel segmentation algorithm for the detection of retinal vessels in funduscopic images is proposed, in which the benefits of both supervised and unsupervised methods are exploited. Ensemble learning based segmentation (ELBS) is employed for the segmentation of large and medium sized vessels, after which a local curve fitting technique is used for the detection of the thin retinal vessels. The general...
State-of-the-art deformable registration algorithms do not perform as well with FA sequences because they are designed to deal with changes of content appearance (e.g., due to different sensors imaging the same organs) but not with content changes, which occur throughout a FA sequence as different portions or the vascular structure are visible (perfused) in different frames. This paper presents a...
Microaneurysms (MAs) are the earliest sign of diabetic retinopathy and manifest as small reddish spots on the retina. Generally, algorithm design for MAs detection starts by separating the vascular system from the background for a posterior analysis of candidate MAs presence. Following this approach, this paper assesses three different methods for vessel segmentation and how they affect posterior...
The proper segmentation of the vascular system of the retina has a very important role in automatic screening systems. Its detection helps the localization of other anatomical parts and also the detection of possible vascular disorders. State-of-the-art machine learning algorithms are reported to have good performance in this field. However, with the spatial resolution of the fundus images growing,...
Characteristic of retinal vasculature has been an important indicator for many diseases such as hypertension and diabetes. A digital image analysis system can assist medical experts to make accurate diagnosis in an efficient manner. This paper presents the computer based approach to the automated segmentation of blood vessels in retinal images. The detection of the retinal vessel is achieved by performing...
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...
Analyzing retinal fundus image is important for early detection of diseases related to the eye. However, in fundus images the contrast between retinal blood vessels and the background is very low. Hence, analyzing or visualizing tiny blood vessels is difficult. Fluorescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this work,...
The categorization of retinal vessels morphological features and the investigation of their branching patterns are used in the process for automated diagnosis and screening of ophthalmologic diseases. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. In this paper, the performance comparison of two retinal vessel...
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...
We present a general framework for vessel segmentation in retinal images with a particular focus on small vessels. The retinal images are first processed by a nonlinear diffusion filter to smooth vessels along their principal direction. The vessels are then enhanced using a compound vessel enhancement filter that combines the eigenvalues of the Hessian matrix, the response of matched filters, and...
The automatic detection of the position of the optic disc is an important step in the automatic analysis of retinal images. A method to detect the approximate position of the optic disc using kNN regression is presented. The method starts by building a regression model of the optic disc position. Using a prior vessel segmentation all vessel pixels are searched for those which are inside the optic...
The automatic detection of the position of the optic disc is an important step in the automatic analysis of retinal images. A method to detect the approximate position of the optic disc using kNN regression is presented. The method starts by building a regression model of the optic disc position. Using a prior vessel segmentation all vessel pixels are searched for those which are inside the optic...
Retinal image vessels can reflect different states of many diseases, which are often used for the registration and mosaic of retinal images. Therefore, it is very important to accurately extract blood vessels. The paper firstly analyzes the characteristic of retinal images, and secondly gray mathematical morphology theories are used to smooth and strengthen retinal images in order to remove the background...
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...
In this paper, we proposed a new scheme for detection of small blood vessels in retinal images. A novel filter called Gabor variance filter and a modified histogram equalization technique are developed to enhance the contrast between vessels and background. Vessel segmentation is then performed on the enhanced map using thresholding and branch pruning based on the vessel structures. The experiments...
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