Using the DCC Lifecycle Model to Curate a Gene Expression Database: A Case Study

Developmental Gene Expression Map (DGEMap) is an EU-funded Design Study, which will accelerate an integrated European approach to gene expression in early human development. As part of this design study, we have had to address the challenges and issues raised by the long-term curation of such a resource. As this project is primarily one of data creators, learning about curation, we have been looking at some of the models and tools that are already available in the digital curation field in order to inform our thinking on how we should proceed with curating DGEMap. This has led us to uncover a wide range of resources for data creators and curators alike. Here we will discuss the future curation of DGEMap as a case study. We believe our experience could be instructive to other projects looking to improve the curation and management of their data

Combined high contrast and wide field-of-view in the Scanning Laser Ophthalmoscope through dual detection of light paths

We demonstrate a multimode detection system in a scanning laser ophthalmoscope (SLO) that enables simultaneous operation in confocal, indirect, and direct modes to permit an agile trade between image contrast and optical sensitivity across the retinal field of view to optimize the overall imaging performance, enabling increased contrast in very wide-field operation. We demonstrate the method on a wide-field SLO employing a hybrid pinhole at its image plane, to yield a twofold increase in vasculature contrast in the central retina compared to its conventional direct mode while retaining high-quality imaging across a wide field of the retina, of up to 200 deg and 20 μm on-axis resolution

Spectral Autofluorescence Imaging of the Retina for Drusen Detection

The presence and characteristics of drusen in retinal images, namely their size, location, and distribution, can be used to aid in the diagnosis and monitoring of Age Related Macular Degeneration (AMD); one of the leading causes for blindness in the elderly population. Current imaging techniques are effective at determining the presence and number of drusen, but fail when it comes to classifying their size and form. These distinctions are important for correctly characterising the disease, especially in the early stages where the development of just one larger drusen can indicate progression. Another challenge for automated detection is in distinguishing them from other retinal features, such as cotton wool spots. We describe the development of a multi-spectral scanning-laser ophthalmoscope that records images of retinal autofluorescence (AF) in four spectral bands. This will offer the potential to detect drusen with improved contrast based on spectral discrimination for automated classification. The resulting improved specificity and sensitivity for their detection offers more reliable characterisation of AMD. We present proof of principle images prior to further system optimisation and clinical trials for assessment of enhanced detection of drusen

Multi-modal retinal scanning to measure retinal thickness and peripheral blood vessels in multiple sclerosis

Our purpose was to investigate changes to the retina in multiple sclerosis (MS) using established and novel modes of retinal image acquisition and analysis. 72 participants with MS and 80 healthy volunteers underwent retinal scanning with optical coherence tomography (OCT) and ultra-widefield (UWF) scanning laser ophthalmoscopy (SLO), over a two-year period. Changes in retinal nerve fibre layer (RNFL) thickness, macular volume and retinal blood vessel diameter were measured and parameters were then tested for associations with MS. Measurements from OCT showed that individuals with MS had a thinner RNFL and reduced macular volume when compared to healthy volunteers. On UWF images, participants with MS had reduced arterial widths in the inferior nasal quadrant of both eyes and reduced venous widths in the inferior nasal quadrant of right eyes. Longitudinal analysis showed that participants with MS had an accelerated annual rate of RNFL thinning in several regions of the retina. In conclusion, the assessment of OCT showed thinning of the RNFL and macula in concordance with previous reports on MS, while analysis of blood vessels in the retinal periphery from UWF-SLO images revealed novel changes

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd

Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management

Retinal area detector from Scanning Laser Ophthalmoscope (SLO) images for diagnosing retinal diseases

© 2014 IEEE. Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%