1,288 research outputs found
NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES
The analysis of retinal vasculature in digital fundus images is important for
diagnosing eye related diseases. However, digital colour fundus images suffer from
low and varied contrast, and are also affected by noise, requiring the use of fundus
angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to
6 time’s higher contrast. However, FFA is an invasive method that requires contrast
agents to be injected and this can lead other physiological problems. A reported
digital image enhancement technique named RETICA that combines Retinex and ICA
(Independent Component Analysis) techniques, reduces varied contrast, and enhances
the low contrast blood vessels of model fundus images
NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM
Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes
mellitus affecting the retina. The pathologies of DR can be monitored by analysing
colour fundus images. However, the low and varied contrast between retinal vessels
and the background in colour fundus images remains an impediment to visual analysis
in particular in analysing tiny retinal vessels and capillary networks. To circumvent
this problem, fundus fluorescein angiography (FF A) that improves the image contrast
is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that
leads to other physiological problems and in the worst case may cause death.
The objective of this research is to develop a non-invasive digital Image
enhancement scheme that can overcome the problem of the varied and low contrast
colour fundus images in order that the contrast produced is comparable to the invasive
fluorescein method, and without introducing noise or artefacts. The developed image
enhancement algorithm (called RETICA) is incorporated into a newly developed
computerised DR system (called RETINO) that is capable to monitor and grade DR
severity using colour fundus images. RETINO grades DR severity into five stages,
namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR
and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image
using RETICA in the macular region and analysing the enlargement of the foveal
avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The
importance of this research is to improve image quality in order to increase the
accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading
through either direct observation or computer assisted diagnosis system
Bilateral diffuse choroidal hemangioma in Sturge Weber syndrome: a case report highlighting the role of multimodal imaging and a brief review of the literature
Purpose: The purpose of this paper is to present a patient with bilateral choroidal hemangioma in Sturge-Weber syndrome (SWS) and highlight multimodal imaging techniques for early detection and management of ocular alterations. Methods: A 37-year-old woman with diagnosis of SWS presented to our unit. The patient had been treated with pulsed dye laser for bilateral nevus flammeus and had right leptomeningeal angiomatosis. She had glaucoma, but ultrasound biomicroscopy did not show anterior chamber or ciliary body alterations. Results: Enhanced depth imaging (EDI) spectral domain optical coherence tomography (SD-OCT) showed bilateral diffuse choroidal hemangiomas in both eyes with choroidal thickness above 1000 μm. B-scan ultrasound examination showed diffuse choroidal hemangioma in both eyes, with a choroidal thickness of 1.53 mm and 1.94 mm in the right and left eye (RE, LE), respectively. Peripapillary retinal nerve fiber evaluation showed thinning of the retinal nerve fiber layer in both eyes. Conclusions: This report highlights multimodal imaging techniques for the critical assessment of patients with SWS, especially in rare cases with bilateral choroidal hemangioma of the choroid. Novel imaging modalities enable optimal management and follow-up of rare conditions, and our case adds further evidence to the existing literature
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
Differential intensity contrast swept source optical coherence tomography for human retinal vasculature visualization
We demonstrate an intensity-based motion sensitive method, called differential logarithmic intensity variance (DLOGIV), for 3D microvasculature imaging and foveal avascular zone (FAZ) visualization in the in vivo human retina using swept source optical coherence tomog. (SS-OCT) at 1060 nm. A motion sensitive SS-OCT system was developed operating at 50,000 A-lines/s with 5.9 μm axial resoln., and used to collect 3D images over 4 mm^2 in a normal subject eye. Multiple B-scans were acquired at each individual slice through the retina and the variance of differences of logarithmic intensities as well as the differential phase variances (DPV) was calcd. to identify regions of motion (microvasculature). En face DLOGIV image were capable of capturing the microvasculature through depth with an equal performance compared to the DPV
In vivo human retinal and choroidal vasculature visualization using differential phase contrast swept source optical coherence tomography at 1060 nm
A differential phase contrast (DPC) method is validated for in vivo human retinal and choroidal vasculature visualization using high-speed swept-source optical coherence tomography (SS-OCT) at 1060 nm. The vasculature was identified as regions of motion by creating differential phase variance (DPV) tomograms: multiple B-scans were collected of individual slices through the retina and the variance of the phase differences was calculated. DPV captured the small vessels and the meshwork of capillaries associated with the inner retina in en face images over 4 mm^2 in a normal subject. En face DPV images were capable of capturing the microvasculature and regions of motion through the inner retina and choroid
Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy
Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes
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