• A description for computed tomography based on sinusoidal curves.

    8 December 2017

    A new description for computed tomography, parallel and equal to Radon transformation but based on sinusoidal functions rather than line integrals, is introduced. This representation contributes an effective algorithm to reduce metal artefacts in reconstruction. Using this method, the information included in the scan data corresponding to the metal implants can be separated from the original projection, an amending method rather than interpolation is therefore provided to reach higher accuracy for reconstruction. The method is with low complexity in computation, and can be combined online with filtered backprojection (FBP), which is the most widely used algorithm in practice, to improve the quality of the reconstructed image. Also, the method has the potential to deal with the artefacts caused by beam hardening and partial volume, and to be developed into a straight reconstruction algorithm based on the sinusoidal representation. Examples are presented for clearer description and demonstration.

  • Accurate robust symmetry estimation

    27 October 2017

    © Springer-Verlag Berlin Heidelberg 1999. There are various applications, both in medical and nonmedical image analysis, which require the automatic detection of the line (2D images) or plane (3D) of reflective symmetry of objects. There exist relatively simple methods of finding reflective symmetry when object images are complete (i.e., completely symmetric and perfectly segmented from image “background”). A much harder problem is finding the line or plane of symmetry when the object of interest contains asymmetries, and may not have well defined edges. A major area of interest is brain image analysis; there are various reasons why one would want to be able to automatically, robustly and accurately find the (sagittal) mid-plane from a 3D brain image. Example applications include pre-alignment (or sanity checking) for standard registration methods, mid-plane finding as part of symmetric probabilistic anatomical map generation, and, in particular, symmetry-based analyses (e.g., for schizophrenia research). This paper describes EROS - Extraction of Robust Orientation using Symmetry, which has been developed to solve this problem. It has been shown to work with MRI (T1, T2, EPI), PET, SPECT and CT, using robust measures to give accurate results even with images containing large asymmetries.

  • A scene segmenter; visual tracking of moving vehicles

    28 November 2017

    In this paper the image processing system ASSET (A Scene Segmenter Establishing Tracking) is described. ASSET receives a sequence of video images taken by a possibly moving camera and segments each image into separately moving objects using image motion. The moving objects are tracked, and their outlines are accurately estimated. The ASSET system provides a useful source of world information, for example, in the area of autonomous vehicle guidance. © 1994.

  • Note on small angle approximations for stereo disparity

    28 November 2017

    A new formula for the stereo disparity field from two tilting, gazing and verging cameras is derived and shown to include terms previously ignored. The importance of these terms is briefly discussed. © 1993.

  • Planar region detection and motion recovery

    12 December 2017

    This paper presents a means of segmenting planar regions from two views of a scene using point correspondences. The initial selection of groups of coplanar points is performed on the basis of conservation of two five point projective invariants (groups for which this invariant is conserved are assumed to be coplanar). The correspondences for four of the five points are used to define a projectivity which is used to predict the change in position of other points assuming they lie on the same plane as the original four. A distance threshold between actual and predicted position is used to find extended planar regions. If two distinct planar regions can be found then a novel motion direction estimator suggests itself. © 1993.

  • A supervised learning process to validate online disease reports for use in predictive models

    15 December 2017

    Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%–79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is being used to validate the data that our system uses to produce updated predictive disease maps on a weekly basis.

  • Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017.

    9 January 2018

    Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential 'inefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%-26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20-28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.

  • Integrating vector control across diseases

    9 January 2018

    Vector-borne diseases cause a significant proportion of the overall burden of disease across the globe, accounting for over 10 % of the burden of infectious diseases. Despite the availability of effective interventions for many of these diseases, a lack of resources prevents their effective control. Many existing vector control interventions are known to be effective against multiple diseases, so combining vector control programmes to simultaneously tackle several diseases could offer more cost-effective and therefore sustainable disease reductions.The highly successful cross-disease integration of vaccine and mass drug administration programmes in low-resource settings acts a precedent for cross-disease vector control. Whilst deliberate implementation of vector control programmes across multiple diseases has yet to be trialled on a large scale, a number of examples of 'accidental' cross-disease vector control suggest the potential of such an approach. Combining contemporary high-resolution global maps of the major vector-borne pathogens enables us to quantify overlap in their distributions and to estimate the populations jointly at risk of multiple diseases. Such an analysis shows that over 80 % of the global population live in regions of the world at risk from one vector-borne disease, and more than half the world's population live in areas where at least two different vector-borne diseases pose a threat to health. Combining information on co-endemicity with an assessment of the overlap of vector control methods effective against these diseases allows us to highlight opportunities for such integration. Malaria, leishmaniasis, lymphatic filariasis, and dengue are prime candidates for combined vector control. All four of these diseases overlap considerably in their distributions and there is a growing body of evidence for the effectiveness of insecticide-treated nets, screens, and curtains for controlling all of their vectors. The real-world effectiveness of cross-disease vector control programmes can only be evaluated by large-scale trials, but there is clear evidence of the potential of such an approach to enable greater overall health benefit using the limited funds available.

  • Prioritising infectious disease mapping

    25 December 2017

    Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available.Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites.A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited.