Search

Nailfold capillaroscopy is a technique for imaging the capillary bed in the finger nailfold, which is used in the diagnosis of scleroderma. Knowledge of the capillary oxygenation profile would be advantageous in disease evaluation due to the suspected involvement of hypoxia in causing fibrosis. A hyperspectral nailfold capillaroscopy system with narrowband illumination provided by a supercontinuum laser and acousto-optic tuneable filter was created to enable spectral analysis of the nailfold. Hyperspectral imaging (HSI) data from 500-595 nm was analysed to extract image quality metrics, which suggested HSI oximetry holds promise for understanding how rheumatic diseases affect oxygenation of the nailfold.

Nailfold capillaroscopy is a technique for imaging the capillary bed in the finger nailfold, that is used in the diagnosis of scleroderma. Knowledge of the capillary oxygenation profile would be a substantial advantage in disease evaluation. A compact, low-cost LED-illuminated capillaroscopy system was conceived based on inexpensive parts and optical hardware. The system uses a compact Raspberry Pi to control a custom-designed LED ring light, with white-light LEDs interleaved with three narrowband LEDs, and a Raspberry Pi camera. Capillary visualisation and distinction of haemoglobin contrast is demonstrated, suggesting future promise for application of multispectral nailfold capillaroscopy in low-resource settings.

A recent study has shown that breast cancer risk can be reduced by taking Tamoxifen, but only if this results in at least a 10% point reduction in mammographic density. When mammographic density is quantified visually, it is impossible to assess reader accuracy using clinical images as the ground truth is unknown. Our aim was to compare three models of assessing density change and to determine reader accuracy in identifying reductions of 10% points or more. We created 100 synthetic, mammogram-like images comprising 50 pairs designed to simulate natural reduction in density within each pair. Model I: individual images were presented to readers and density assessed. Model II: pairs of images were displayed together, with readers assessing density for each image. Model III: pairs of images were displayed together, and readers asked whether there was at least a 10% point reduction in density. Ten expert readers participated. Readers' estimates of percentage density were significantly closer to the truth (6.8%-26.4%) when images were assessed individually rather than in pairs (9.6%-29.8%). Measurement of change was significantly more accurate in Model II than Model I (p

We address the problem of evaluating the performance of algorithms for detecting curvilinear structures in medical images. As an exemplar we consider the detection of vessel trees which contain structures of variable width and contrast. Results for the conventional approach to evaluation, in which the detector output is compared directly with a ground-truth mask, tend to be dominated by the detection of large vessels and fail to capture adequately whether or not finer, lower contrast vessels have been detected successfully. We propose and investigate three alternative evaluation strategies. We demonstrate the use of the standard and new evaluation strategies to assess the performance of a novel method for detecting vessels in retinograms, using the publicly available DRIVE database.

The quantity and appearance of dense breast tissue in mammograms is related to the risk of developing breast cancer, the sensitivity of mammographic interpretation, and the likelihood of local recurrence of cancer following surgery. Visual assessment of breast density is widely used, often with readers indicating the percentage of dense tissue in a mammogram. Although real mammograms can be used to investigate intra- and inter-observer variability, ground truth is difficult to ascertain, so to investigate reader accuracy, we created 60 synthetic, mammogram-like images with densities comparable in area to those found in screening. The images contained either a single dense area, multiple or linear densities, or a variable breast size with a single density. The images were randomized and assessed by 9 expert and 6 non-expert readers who marked percentage area of density on a visual analogue scale. Non-expert readers' estimates of percentage area of density were closer to the truth (6-11% mean absolute difference) than the experts' estimates (10- 19%). The readers were most accurate when the density formed a single area in the image, and least accurate when the dense area was composed of linear structures. In almost every case, the dense area was overestimated by the expert readers. When experts were ranked according to the degree of overestimation, this broadly reflected their relative performance on real mammograms.

Breast density is positively linked to the risk of developing breast cancer. We have developed a semi-automated, stepwedge-based method that has been applied to the mammograms of 1,289 women in the UK breast screening programme to measure breast density by volume and area. 116 images were analysed by three independent operators to assess inter-observer variability; 24 of these were analysed on 10 separate occasions by the same operator to determine intra-observer variability. 168 separate images were analysed using the stepwedge method and by two radiologists who independently estimated percentage breast density by area. There was little intra-observer variability in the stepwedge method (average coefficients of variation 3.49% - 5.73%). There were significant differences in the volumes of glandular tissue obtained by the three operators. This was attributed to variations in the operators' definition of the breast edge. For fatty and dense breasts, there was good correlation between breast density assessed by the stepwedge method and the radiologists. This was also observed between radiologists, despite significant inter-observer variation. Based on analysis of thresholds used in the stepwedge method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10 and 20% of the total thickness of tissue.

A method has been developed for generating synthetic masses that exhibit the appearance of real breast cancers in mammograms. To be clinically useful, the synthetic masses must appear sufficiently realistic, even to expert mammogram readers. This paper presents the results of an observer study in which 10 expert mammogram readers at the Nightingale Centre, Manchester attempted to distinguish between real and synthetically generated masses. Each reader rated a set of 30 real and 30 synthetics masses on a scale ranging from "definitely real" to "definitely synthetic". ROC curves were fitted to their responses and the area-under-curve (AUC) used to quantify the ability of a reader to identify synthetic masses. The mean AUC was 0.70±0.09, showing the readers were able to identify synthetic masses at a rate statistically better than chance and suggesting that further improvements must be made to the mass synthesis method. Analysis of individual AUC scores showed reader performance was not affected by job type (radiologist versus breast physician/radiographer) or experience.