Sir Robert Hart Photo Collection on Flickr

The photographs in this album were selected with the assistance of the Sir Robert Hart Research Project, which is a collaboration between Special Collections & Archives in the Library, the School of Modern History & Anthropology, Queen’s University Belfast, and the Institute of Modern History at the Chinese Academy of Social Sciences (CASS) in Beijing. The research project is creating an annotated photobook from the Sir Robert Hart Photo Collection (originally donated in the 1970s) and the Irons Collection. The photographs here reflect those that will be included in the book.

Brutus

This article discusses the eponymous legendary founder of Britain, Brutus, a descendent of the Trojan Aeneas and first king of Britain. It tracks the emergence of Brutus in historiography and literature from the seventh to the sixteenth centuries, from the Historia Brittonum, via the Brut tradition and Geoffrey of Monmouth’s Historia Regum Britannia, to the poem Sir Gawain and the Green Knight, Caxton’s Chronicles of England, Milton’s The History of Britain and Spenser’s The Fairie Queene.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.