Advanced Artery / Vein Classification System in Retinal Images for Diabetic Retinopathy

Diabetic retinopathy is that the single largest explanation for sight loss and visual impairment in eighteen to sixty five year olds. Screening programs for the calculable 1 to 6 % of the diabetic population are incontestable to be value and sight saving, but unfortunately there are inadequate screening resources. An automatic screening system might facilitate to solve this resource short fall.The retinal vasculature consists of the arteries and veins with their tributaries that are visible at intervals in the retinal images.This paper proposes a graphbased artery vein classification system inretinal images for diabetic retinopathybased on the structural informationextracted from the retinalvasculature. The method at first extracts agraph from the vascular tree and then makes a decision on the typeof each intersection point (graph node).Based on this node types one of the twolabels are assigned to each vessel segment.Finally, the A/V classes are assigned tothe sub graph labels by extracting a set ofintensity features and using artificialneural network. DOI: 10.17762/ijritcc2321-8169.15017

Vessel labeling in combined confocal scanning laser ophthalmoscopy and optical coherence tomography Images : criteria for blood vessel discrimination

INTRODUCTION: The diagnostic potential of optical coherence tomography (OCT) in neurological diseases is intensively discussed. Besides the sectional view of the retina, modern OCT scanners produce a simultaneous top-view confocal scanning laser ophthalmoscopy (cSLO) image including the option to evaluate retinal vessels. A correct discrimination between arteries and veins (labeling) is vital for detecting vascular differences between healthy subjects and patients. Up to now, criteria for labeling (cSLO) images generated by OCT scanners do not exist. OBJECTIVE: This study reviewed labeling criteria originally developed for color fundus photography (CFP) images. METHODS: The criteria were modified to reflect the cSLO technique, followed by development of a protocol for labeling blood vessels. These criteria were based on main aspects such as central light reflex, brightness, and vessel thickness, as well as on some additional criteria such as vascular crossing patterns and the context of the vessel tree. RESULTS AND CONCLUSION: They demonstrated excellent inter-rater agreement and validity, which seems to indicate that labeling of images might no longer require more than one rater. This algorithm extends the diagnostic possibilities offered by OCT investigations

Human retinal oximetry using hyperspectral imaging

The aim of the work reported in this thesis was to investigate the possibility of measuring human retinal oxygen saturation using hyperspectral imaging. A direct non-invasive quantitative mapping of retinal oxygen saturation is enabled by hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated haemoglobin are recorded and analysed. Implementation of spectral retinal imaging thus requires ophthalmic instrumentation capable of efficiently recording the requisite spectral data cube. For this purpose, a spectral retinal imager was developed for the first time by integrating a liquid crystal tuneable filter into the illumination system of a conventional fundus camera to enable the recording of narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing algorithms were developed to enable accurate exploitation of spectral retinal images and overcome the confounding problems associated with this technique due to the erratic eye motion and illumination variation. Several algorithms were developed to provide semi-quantitative and quantitative oxygen saturation measurements. Accurate quantitative measurements necessitated an optical model of light propagation into the retina that takes into account the absorption and scattering of light by red blood cells. To validate the oxygen saturation measurements and algorithms, a model eye was constructed and measurements were compared with gold-standard measurements obtained by a Co-Oximeter. The accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated blood samples. Clinical trials from healthy and diseased subjects were analysed and oxygen saturation measurements were compared to establish a merit of certain retinal diseases. Oxygen saturation measurements were in agreement with clinician expectations in both veins (48%±9) and arteries (96%±5). We also present in this thesis the development of novel clinical instrument based on IRIS to perform retinal oximetry.Al-baath University, Syri

Theranostic Window to the Brain for Multispectral Light Delivery and Microcirculation Imaging

Interest in using optical methods in the development of noninvasive clinicaldiagnostic and therapeutic techniques for brain diseases has widely increased due to theirsimplicity, safety, and affordability. The main limitations of light-based techniques usedfor brain theranostics are the strong light scattering in the scalp and skull, which causedecrease of spatial resolution, low contrast, and small penetration depth. To address thischallenge, our group has previously introduced a transparent nanocrystallineyttria-stabilized-zirconia (nc-YSZ) cranial implant material which implant possesses themechanical strength and biocompatibility that are prerequisites for a clinically-viablepermanent cranial implant for patients.This implant possesses the mechanical strength and biocompatibility that areprerequisites for a clinically-viable permanent cranial implant for patients. A potentialbenefit of this optical window is an improvement of light-based therapeutic techniquesthat rely on sufficient light penetration to a target embedded in tissue such asphotobiomodulation, photodynamic therapy, and optogenetics. Another application of thisoptical window is noninvasive visualization of brain blood vessels, hematomas, and smallpathologic structures (including cancerous growth) with high resolution. This isimportant for diagnosis and treatment of many diseases such as tumors of the brain,vascular pathologies, and so forth. In this dissertation, I investigated (1) characteristicsand durability of transparent nc-YSZ implants; (2) feasibility of chronic brain imagingthrough the implant; (3) multimodal imaging across the implant to generate anarteriovenous vascular map; (4) through-scalp VIS-NIR light delivery and microvascularimaging

A novel automated approach of multi-modality retinal image registration and fusion

Biomedical image registration and fusion are usually scene dependent, and require intensive computational effort. A novel automated approach of feature-based control point detection and area-based registration and fusion of retinal images has been successfully designed and developed. The new algorithm, which is reliable and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The relative study of retinal images enhances the information on the fundus image by superimposing information contained in the angiogram image. Through the thesis research, two new contributions have been made to the biomedical image registration and fusion area. The first contribution is the automatic control point detection at the global direction change pixels using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. The second contribution is the heuristic optimization algorithm that maximizes Mutual-Pixel-Count (MPC) objective function. The initially selected control points are adjusted during the optimization at the sub-pixel level. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. To our knowledge, it is the first time that the MPC concept has been introduced into biomedical image fusion area as the measurement criteria for fusion accuracy. The fusion image is generated based on the current control point coordinates when the iteration stops. The comparative study of the presented automatic registration and fusion scheme against Centerline Control Point Detection Algorithm, Genetic Algorithm, RMSE objective function, and other existing data fusion approaches has shown the advantage of the new approach in terms of accuracy, efficiency, and novelty

Human retinal oximetry using spectral imaging

The principal aim of the research described in this thesis was to develop a technique of non-invasively measuring the oxygen saturation within the retinal vasculature of human subjects (retinal oximetry). The evaluation of a hyperspectral fundus camera used to acquire retinal images in different wavelengths of visible light, and the image analysis techniques used to perform retinal oximetry are described. Validation of the oximetry techniques was performed using an artificial eye containing human blood of known oxygen saturation: the calculated oxygen saturation was compared to the gold standard measurement. The mean differences between the calculated and measured oxygen saturations were small. Hyperspectral imaging/oximetry of normal subjects was performed to characterize the oximetric features of the retinal vasculature. The mean oxygen saturation (± SD) of the temporal retinal arterioles and venules were 110.8% (± 11.8%) and 27.7% (± 3.2%) respectively. The application of the retinal oximetry technique was explored in patients with retinal arterial and venous occlusion to determine whether oximetric changes in the retinal vasculature could be detected. Variation in measured oxygen saturation of the retinal arterioles and venules respectively were apparent, and corresponded with angiographic features of retinal capillary loss. The techniques were applied to patients with asymmetrical primary open angle glaucoma to determine whether oximetric changes could be detected. The mean oxygen saturation of the temporal retinal venules were significantly higher [44.8% (± 24.2%)] in the more advanced glaucomatous eyes compared to normal subjects. Hyperoxia of the retinal venules suggests reduced oxygen consumption as a consequence of inner retinal dysfunction in glaucoma. However, because of the small sample size, further research on a larger population of subjects is required to support this finding. Hyperspectral imaging could be used to detect oximetric abnormalities in the retinal vasculature in patients with retinovascular occlusion and glaucoma

Imaging of Hypoxia in Retinal Vascular Disease

Retinal tissue hypoxia is a key mediator in the pathogenesis of many leading causes of irreversible vision loss, including diabetic retinopathy. Retinal hypoxia in diabetic retinopathy has been shown to drive the production of pro-inflammatory cytokines and pro-angiogenic growth factors. Together, these factors contribute to disease progression by causing unregulated growth of new blood vessels, increased vascular permeability and cell death within the retina. Studies have shown that retinal hypoxia precedes many of the pathologic events that occur during the progression of diabetic retinopathy such as angiopathy, microaneurysms, and capillary dropout. Therefore, early detection of hypoxia in the retinas of diabetic patients could help clinicians identify problems in patients before irreversible damage has occurred. Currently, oxygen sensitive electrodes remain the gold standard for direct measurement of oxygen tension within the retinal tissue; however the procedure is highly invasive and is therefore limited in its applicability towards preclinical models. Less invasive techniques such as retinal oximetry, phosphorescence-lifetime imaging, and hypoxia-sensitive fluorescent probes have shown promising diagnostic value in facilitating detection of oxygen imbalance correlated with neurovascular dysfunction in DR patients. This review highlights the current progress and potential of these minimally invasive hypoxia-imaging techniques in diabetic retinopathy

The Retinal Microvasculature in Secondary Progressive Multiple Sclerosis

In light of new data regarding pathology of multiple sclerosis (MS), more research is needed into the vascular aspects of the disease. Demyelination caused by inflammation is historically thought of as the main cause of disability in the disease. Recent studies, however, have suggested that MS is in fact a spectrum of overlapping phenotypes consisting of inflammation, oxidative damage and hypoperfusion. The microvasculature plays an important role in all of these pathogenic processes and its dysfunction may therefore be of crucial importance to the development and progression of the disease. This thesis focuses on investigating the microvasculature of the retina as a surrogate for the brain by assessing the vascular structure, blood flow dynamics and oxygen transfer of the retinal blood vessels in secondary progressive multiple sclerosis (SPMS). Studying the retinal microvasculature using a multimodal imaging approach has allowed us to develop a more detailed understanding of blood flow in MS and to identify new imaging markers for trials into neuroprotective drugs in MS. The work done in this thesis demonstrated; i) a higher rate of retinal microvascular abnormalities in MS which progresses with disease severity, ii) evidence of retinal vascular remodelling in SPMS and iii) changes in blood velocity and flow in the retina in SPMS. These observations pave the way for future investigations into the mechanisms of vascular alterations and vascular dysfunction in MS, and provide a set of imaging markers to further explore other cerebrovascular diseases through the retina

Discovery of retinal biomarkers for vascular conditions through advancement of artery-vein detection and fractal analysis

Research into automatic retina image analysis has become increasingly important, not just in ophthalmology but also in other clinical specialities such as cardiology and neurology. In the retina, blood vessels can be directly visualised non-invasively in-vivo, and hence it serves as a "window" to cardiovascular and neurovascular complications. Biomarker research, i.e. investigating associations between the morphology of the retinal vasculature (as a means of revealing microvascular health or disease) and particular conditions affecting the body or brain could play an important role in detecting disease early enough to impact on patient treatment and care. A fundamental requirement of biomarker research is access to large datasets to achieve sufficient power and significance when ascertaining associations between retinal measures and clinical characterisation of disease. Crucially, the vascular changes that appear can affect arteries and veins differently. An essential part of automatic systems for retinal morphology quantification and biomarker extraction is, therefore, a computational method for classifying vessels into arteries and veins. Artery-vein classification enables the efficient extraction of biomarkers such as the Arteriolar to Venular Ratio, which is a well-established predictor of stroke and other cardiovascular events. While structural parameters of the retinal vasculature such as vessels calibre, branching angle, and tortuosity may individually convey some information regarding specific aspects of the health of the retinal vascular network, they do not convey a global summary of the branching pattern and its state or condition. The retinal vascular tree can be considered a fractal structure as it has a branching pattern that exhibits the property of self-similarity. Fractal analysis, therefore, provides an additional means for the quantitative study of changes to the retinal vascular network and may be of use in detecting abnormalities related to retinopathy and systemic diseases. In this thesis, new developments to fully automated retinal vessel classification and fractal analysis were explored in order to extract potential biomarkers. These novel processes were tested and validated on several datasets of retinal images acquired with fundus cameras. The major contributions of this thesis include: 1) developing a fully automated retinal blood vessel classification technique, 2) developing a fractal analysis technique that quantifies regional as well as global branching complexity, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies