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Background: Maintain Your Brain (MYB) is a randomized controlled trial of an online multi-modal lifestyle intervention targeting modifiable dementia risk factors with its primary aim being to reduce cognitive decline in an older age cohort. Methods: MYB aims to recruit 8,500 non-demented community dwelling 55 to 77 year olds from the Sax Institute's 45 and Up Study in New South Wales, Australia. Participants will be screened for risk factors related to four modules that comprise the MYB intervention: physical activity, nutrition, mental health, and cognitive training. Targeting risk factors will enable interventions to be personalized so that participants receive the most appropriate modules. MYB will run for three years and up to four modules will be delivered sequentially each quarter during year one. Upon completing a module, participants will continue to receive less frequent booster activities for their eligible modules (except for the mental health module) until the end of the trial. Discussion: MYB will be the largest internet-based trial to attempt to prevent cognitive decline and potentially dementia. If successful, MYB will provide a model for not just effective intervention among older adults, but an intervention that is scalable for broad use.

Fixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such, one expects a high similarity between FDM created in different laboratories. So far, no studies have analyzed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modeling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community.

Children with life limiting conditions require intensive, complex management of the physical, psychosocial and spiritual issues that evolve for the child and their family as the focus of care moves from curative treatment to palliation. Optimal palliative care for children can be limited by the lack of health professionals with the necessary skills and experience to confidently and competently manage the care of both the child and the family. The University of Queensland's Centre for Online Health has been working closely with the Paediatric Palliative Care Service (PPCS) at the Royal Children's Hospital (RCH) in Brisbane, Australia, to investigate the potential of telemedicine for the delivery of specialist support in the home or local community. Research conducted in Queensland has shown how telemedicine can be effectively used to link the specialist paediatric palliative care team into the home of families caring for a child receiving palliative care, regardless of their physical distance from the RCH. This qualitative report describes two case studies, each illustrating the value of home telemedicine for families at this vulnerable and distressing time. This work should provide a useful insight for other specialty disciplines contemplating home telemedicine applications.

This chapter consists of two main parts. Firstly a framework for the adaptive dis-play of mobility information for visual prosthesis recipients is presented. In the second part the application of this framework in practice is demonstrated with a simulated visual prosthesis mobility experiment.

Introduction. Coronary heart disease is the primary cause of sudden death in Australia, and is predicted to become the single leading health problem for the world by 2020 [1]. This disease results from an accumulation of plaque (consisting of cholesterol, calcium, and other substances) on the interior surface of arteries supplying the heart. Multi-slice spiral computed tomography angiography (MS-CTA) is emerging as a non-invasive alternative to the current "gold-standard" -- catheter-based coronary angiography (CCA) [2]. To effectively employ MS-CTA as a diagnostic tool, cardiologists use a variety of post-processing software algorithms to measure the degree of artery blockage (known as stenosis). Currently a number of algorithms exist including: oblique sectioning, curved sectioning, thin-slab maximum intensity projection (MIP), and surface rendering [3]. Methods. Unfortunately the current algorithms are inherently limited for providing insight into the complex three dimensional nature of diseased coronary arteries. These limitations stem from the two dimensional nature of these methods and/or their inability to clearly distinguish the vessel lumen from calcification artefacts. This work proposes the combined use of vessel segmentation and direct volume rendering (DVR) as a post-processing technique for more easily assessing stenosis within MS-CTA datasets. We present an initial investigation into a tracking-based vessel segmentation algorithm, coupled with direct volume rendering for visualisation. Results. A number of synthetic datasets were generated for the purpose of developing and verifying the tracking-based segmentation algorithm. Visual inspection of initial results from these synthetic datasets demonstrates the usefulness and potential of the proposed method. Further, we discuss a number of issues and present preliminary results for applying the segmentation method to real MS-CTA datasets. Conclusions. An initial investigation was undertaken into the usef

Image fusion provides a mechanism to combine multiple images into a single representation to aid human visual perception and image processing tasks. Such algorithms endeavour to create a fused image containing the salient information from each source image, without introducing artefacts or inconsistencies. Image fusion is applicable for numerous fields including: defence systems, remote sensing and geoscience, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualisation of multi-modal datasets. This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a 'plug-and-play' environment for the construction of n-D multi-scale image fusion methods. We give a brief overview of the toolkit design and demonstrate how to construct image fusion algorithms from low-level components (such as multi-scale methods and feature generators). A number of worked examples for medical applications are presented in Appendix A, including quadrature mirror filter discrete wavelet transform (QMF DWT) image fusion.

This paper describes a series of experiments to investigate influence of perceptual response in skilled observers, due to subtle pixel intensity transforms in radiological images. Contrast and edge enhancement operations were applied to digitized mammograms, in order to determine thresholds at which variations in attentional behaviour not consciously identified by the observer were detected, during normal visual scanning procedures in a typical screening viewing situation. Continuous tracking of eye movements was undertaken to obtain patterns of fixation sequences and durations for three different observers, and both qualitative and quantitative analyses were applied to this data. Consistent thresholds at which attentional perturbation occurred were established based on levels of aggregated pixel errors determined by SNR values, across the different methods of image manipulation considered.

Mutual information (MI) is a popular entropy-based similarity measure used in the medical imaging field for multimodal registration. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximize the MI between two images. A common implementation of MI involves the use of Parzen windows. This process generally requires two samples of image intensities: one to estimate the underlying intensity distributions and the second to estimate the entropy. This paper presents a novel gradient-based registration algorithm (MIGH) which uses Gauss-Hermite quadrature to estimate the image entropies. The use of this technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities. With this technique, it is possible to achieve similar results and registration accuracy when compared to current Parzen-basedMI techniques. These results are achieved using half the previously required sample sizes and also with an improvement in algorithm complexity.

Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between images acquired from different individuals or atlases, among others. This type of registration defines a deformation field that gives a translation or mapping for every pixel in the image. One popular local approach for estimating this deformation field, known as block matching, is where a grid of control points are defined on an image and are each taken as the centre of a small window. These windows are then translated in the second image to maximise a local similarity criterion. This generates two corresponding sets of control points for the two images, yielding a sparse deformation field. This sparse field can then be propagated to the entire image using methods such as the thin-plate spline warp or simple Gaussian convolution. Previous block matching procedures all utilise uniformly distributed grid points. This results in the generation of a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. That is, results are better in regions of high information when compared to regions of low information. Consequently, this paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo (RJMCMC) statistical procedure to optimally select grid points of interest. These grid points have a greater concentration in regions of high information and a lower concentration in regions of small information. Results show that non-rigid registration can by improved by using optimally selected grid points of interest.

This paper investigates methods of processing mobility related static images to enhance the effectiveness of a visual prosthesis system. Eight images were processed into 50x50 pixel binary, greyscale, Sobel and Canny edge detected images. 10 subjects were asked 5 mobility related identification tasks for each (randomly ordered) image. Results indicate that edge detection may be useful at this resolution. However, there was not a significant difference found between the results achieved using the Canny and Sobel algorithms. These results support the development of an adaptive device. A mobility display framework has been proposed to assist in this development. Future work will focus on processing image sequences and the development of a visual prosthesis simulation device.

This paper discusses an application of an intelligent information system for decision-makers involved in multiple criteria group site selection problems under uncertainty. The fuzzy algorithm behind the system was developed by the authors for use in large-scale infrastructure site selection, and is validated here using a site selection problem at Brisbane Airport. The ‘InfraPlanner’ Spatial Decision Support System (SDSS) was created by customising ArcView GIS, and operates on raster data files. The tightly coupled system features linguistic interaction, multiple decision-maker input, uncertainty assessment, and a linguistically controllable aggregation function capable of a variety of compensatory and non-compensatory outcomes. Feedback from decision-makers involved in the experiment indicated a high level of satisfaction with outputs from the system, whilst noting some areas for future development.

Feature detectors in the early preattentive stage of the Human Visual System (HVS) are believed to cause regions of the viewing field to be identified as perceptually salient, attracting the attention of the viewer. It is anticipated that this characteristic of the HVS can be incorporated into a feature based fuzzy scene decomposition model, which will assist an image rendering system in the allocation of the highest levels of detail to the most conspicuous objects. Efficiency gains should occur, with minimal loss of perceptual image quality. This paper describes the early stages of the development of this fuzzy model, for a small subset of commonly accepted visual features: colour, size, location, edges and depth cues. Previous researchers have used arbitrary feature relationship models in image processing systems, with some success. Our aim is to improve on these models by integrating present knowledge of visual feature relationships , with experimental results of our own, and to apply this model to the area of image synthesis. Preliminary results from experiments with size features are presented, along with planned experimentation for other visual features. This work will have applications in the areas of scientific visualisation, vision simulation and entertainment.

Feature detectors in the early preattentive stage of the Human Visual System (HVS) are believed to cause regions of the viewing field to be identified as perceptually salient, attracting the attention of the viewer. It is anticipated that this characteristic of the HVS can be incorporated into a feature based fuzzy scene decomposition model, which will assist an image rendering system in the allocation of the highest levels of detail to the most conspicuous objects. Efficiency gains should occur, with minimal loss of perceptual image quality. This paper describes the early stages of the development of this fuzzy model, for a small subset of commonly accepted visual features: colour, size, location, edges and depth cues. Previous researchers have used arbitrary feature relationship models in image processing systems, with some success. Our aim is to improve on these models by integrating present knowledge of visual feature relationships , with experimental results of our own, and to apply this model to the area of image synthesis. Preliminary results from experiments with size features are presented, along with planned experimentation for other visual features. This work will have applications in the areas of scientific visualisation, vision simulation and entertainment.

Williams, P.A.H., & Maeder, A.J. (2015). Security and privacy issues for mobile health. In Adibi, S. (Ed.) Mobile health: A technology road map, Volume 5 (pp. 1067-1088). Cham, Switzerland: Springer International Publishing. Available here.