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This paper presents a new idea in image segmentation algorithm driven by human visual system segmentation techniques and has been widely studied in the visual attention. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size. we propose a method to segment the object of interest by finding the optimal closed contour...
With the sophistication in automated computing systems Bio-Medical Image analysis is made simple. Today there is an increase in interest for setting up medical system that can screen a large number of people for sight threatening diseases, such Retinoblastoma (Rb) and Diabetic Retinopathy(DR). Spatial Domain Edge Detection approach needs Gray scale images for feature extraction and highly prone to...
A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into...
This article presents a novel photoreceptor detection algorithm applied to in-vivo Adaptive Optics (AO) images of the retina obtained from an advanced ophthalmic diagnosis device. Our algorithm is based on a recursive construction of thresholded connected components when the seeds of the recursions are the regional maxima of the image. This algorithm results in a labeling of the AO image which is...
This paper presents a novel unsupervised vascular segmentation algorithm which is applied to retinal fundus images, however could be generalised to any two-dimensional vascular image. The algorithm presents a new fully automatic framework for vessel segmentation and comprises the following features: novel application of the NPWindows method for intensity distribution estimation on localised `image...
Retinal image analysis is currently a very vivid field in biomedical image analysis. One of the most challenging tasks is the reliable automatic detection of microaneurysms (MAs). Computer systems that aid the automatic detection of diabetic retinopathy (DR) greatly rely on MA detection. In this paper, we present a method to construct an MA score map, from which the final MAs can be extracted by simple...
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...
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...
Choroidal Neovascularization (CNV) is a severe retinal disease characterized by abnormal growth of blood vessels in the choroidal layer. Current diagnosis of CNV depends mainly on qualitative assessment of a temporal sequence of fundus fluorescein angiography images. Automated segmentation and identification of the CNV lesion types (either occult or classic) is required to reduce the inter-and intra-...
The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation...
Analyzing high-resolution images of astrocytes is important in understanding the diseases, such as glaucoma and retinal detachment, to which astrocytes are known to become reactive. This is challenging because the cells are small, homogeneous, and closely packed. We propose an interactive visualization system designed for such images. Our system employs a probabilistic segmentation algorithm to help...
The morphology of retinal blood vessels contains valuable information for the diagnosis of retinal dysfunctions. The vessels can be segmented from color fundus images but the connectivity of the segmented vessels is not always preserved because of low contrast, imaging noise and artifacts. If a continuous vessel is interpreted as multiple disjoint vessel segments, the morphological measurements such...
Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labelled lesion training sets which are time consuming to create,...
This paper proposes a messy model based watermarking scheme for the authentication of medical images. The digital fundus images are one particular class of medical images which has been chosen for simulation and analysis of the proposed scheme in this paper. These images are given in TIF format in RGB color space. The proposed scheme dynamically generates the watermark using messy models. And, it...
Smart cards are increasingly being used as a form of identification and authentication. One inherent problem with smart cards, however, is the possibility of loss or theft. Current options for securing smart cards against unauthorized use are primarily restricted to passwords. Passwords are easy enough for others to steal so that they do not offer sufficient protection. This has promoted interest...
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...
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...
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 neural network for classifying, which four features of this...
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