Accurate segmentation and evaluation of the anatomical and pathological features of retinal vessels are critical for the diagnosis and study of many ocular diseases. These include retinopathy of prematurity (ROP). ROP is a disorder of the retinal blood vessels that is a major cause of vision loss in premature neonates. It is proposed a methodology for extracting the vascular network in the huma...

In both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of retinal vessel tree has become prerequisite step for automatic or computer-aided diagnosis systems. Unlike typical foreground background in normal image processing, there are three problems making task even harder. First, color tends to be red everywhere, thus having a lower contrast than segmentation. S...

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achi...

In this paper we present a method for fovea localization which does not use organization information of other retinal structures like optic disk and arcades. The main advantage of this method is that it does not require segmentation/localization of other retinal structures which are required as prior knowledge in existing fovea localization methods. The key idea is to enhance the relative contr...

ISSN: 2186-1390 (Online) http://www.ijcvsp.com Abstract Analysis of retinal blood vessels allows us to identify individuals with the onset of cardiovascular diseases, diabetes and hypertension. Unfortunately, this analysis requires a specialist to identify specific retinal features which is not always possible. Automation of this process will allow the analysis to be performed in regions where ...

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one dev...

The independent motions of objects in a visual scene are commonly manifest as overlapping retinal motions. A consequence of this overlap is the creation of spurious retinal image features--such as corners and terminated contours--that bear no direct relation to the motions of the objects that give rise to them. To reconstruct object motions, these emergent features must be distinguished from th...

Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age. For monitoring the pathology image registration algorithms applied to retinal images is very useful. In this work, a novel vessel-based retinal image registration approach is proposed. The segmentation of the vasculature is performed by a multi-agent system model. All these information is t...

The thesis aims to investigate the performance and potential of wavelets in the context of feature detection in retinal imaging, and to use those results to develop a robust algorithm that can reliably detect the blood vessels within retinal photographs. Detection of blood vessels in retinal images is an important step in the predictions and diagnosis of cardio-vascular diseases, such as hypert...

Diabetic retinopathy (DR) is one of the diseases that cause blindness globally. Untreated accumulation fat and cholesterol may trigger atherosclerosis in diabetic patient, which obstruct blood vessels. Retinal fundus images are used as diagnostic tools to screen abnormalities linked affect eye. Blurriness low contrast major problems when segmenting retinal images. This article proposes an algor...