Towards the optimal Bayes classifier using an extended self-organising map

In this paper, we propose an extended self-organising learning scheme, in which both distance measure and neighbourhood function have been replaced by the neuron's posterior probabilities. Updating of weights is within a limited but fixed sized neighbourhood of the winner. Each unit will converge to one component of a mixture distribution of input samples, so that an optimal pattern classifier can be formed. The proposed learning scheme can be used to train other forms of unsupervised networks, such as radial-basis-function networks. An application example on textured image segmentation is presented

PRaVDA: seeing and treating cancer with protons

Annually over 300, 000 people are diagnosed with cancer in the UK. Radiotherapy accounts for 40% of those either cured or for whose survival has been prolonged, by using ionising radiation, of which protons are the most effective form, to kill cancer cells with minimal damage to healthy tissue. To guide and confirm treatment, we will need some of the most demanding medical imaging technology to date. The exhibit will outline the science and technology behind PRaVDA

Computerized tomography systems and methods

Some embodiments of the present invention provide apparatus for detecting particles of radiation comprising: a plu rality of solid state semiconductor detector devices provided at spaced apart locations along a beam axis, the detector devices each being configured to generate an electrical signal indicative of passage of a particle through or absorption of a particle by the device; and at least one absorber portion configured to absorb at least a portion of an energy of a particle, where in one said at least one absorber portion is provided in a particle path between at least one pair of adjacent detector devices, the apparatus being configured to provide an output signal indicative of the energy of a particle, the output signal provided being dependent on the electrical signals indicative of passage of a particle through or absorption of a particle by the devices

Relating vanishing points to catadioptric camera calibration

This paper presents the analysis and derivation of the geometric relation between vanishing points and camera parameters of central catadioptric camera systems. These vanishing points correspond to the three mutually orthogonal directions of 3D real world coordinate system (i.e. X, Y and Z axes). Compared to vanishing points (VPs) in the perspective projection, the advantages of VPs under central catadioptric projection are that there are normally two vanishing points for each set of parallel lines, since lines are projected to conics in the catadioptric image plane. Also, their vanishing points are usually located inside the image frame. We show that knowledge of the VPs corresponding to XYZ axes from a single image can lead to simple derivation of both intrinsic and extrinsic parameters of the central catadioptric system. This derived novel theory is demonstrated and tested on both synthetic and real data with respect to noise sensitivity

YODL2 : Developing a search interface for multimedia content at the University of York

At the University of York, the Digital Library (YODL) have committed to using Fedora as the repository for preservation of its digital content. Our requirements for storing rich multimedia content along with the need to apply a diverse set of access control policies had initially lead us to trial Muradora for the search and access interface. For a number of reasons, we are now moving to a new interface, being developed in house during 2010 to replace our current Muradora-based interface. This paper presents our requirements, design decisions and challenges faced implementing the new search interface – YODL2, focusing on the image search aspect of our multimedia repository

The use of field-programmable gate arrays for the hardware acceleration of design automation tasks

This paper investigates the possibility of using Field-Programmable Gate Arrays (Fr’GAS) as reconfigurable co-processors for workstations to produce moderate speedups for most tasks in the design process, resulting in a worthwhile overall design process speedup at low cost and allowing algorithm upgrades with no hardware modification. The use of FPGAS as hardware accelerators is reviewed and then achievable speedups are predicted for logic simulation and VLSI design rule checking tasks for various FPGA co-processor arrangements

Regression analysis for paths inference in a novel Proton CT system

In this work, we analyse the proton paths inference for the construction of CT imagery based on a new proton CT proton system, which can record multiple proton paths/residual energies. Based on the recorded paths of multiple protons, every proton path is inferred. The inferred proton paths can then be used for the residual energies detection and CT imagery construction for analyzing a specific tissue. Different regression methods (linear regression and Gaussian process regression models) are exploited for the path inference of every proton in this work. The studies on a recorded proton trajectories dataset show that the Gaussian process regression method achieves better accuracies for the path inference, from both path assignment accuracy and root mean square errors (RMSEs) studies

Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image

Owing to the inconsistent image quality existing in routine obstetric ultrasound (US) scans that leads to a large intraobserver and interobserver variability, the aim of this study is to develop a quality-assured, fully automated US fetal head measurement system. A texton-based fetal head segmentation is used as a prerequi- site step to obtain the head region. Textons are calculated using a filter bank designed specific for US fetal head structure. Both shape- and anatomic-based features calculated from the segmented head region are then fed into a random forest classifier to determine the quality of the image (e.g., whether the image is acquired from a correct imaging plane), from which fetal head measurements [biparietal diameter (BPD), occipital–frontal diam- eter (OFD), and head circumference (HC)] are derived. The experimental results show a good performance of our method for US quality assessment and fetal head measurements. The overall precision for automatic image quality assessment is 95.24% with 87.5% sensitivity and 100% specificity, while segmentation performance shows 99.27% (`0.26) of accuracy, 97.07% (`2.3) of sensitivity, 2.23 mm (`0.74) of the maximum symmetric contour distance, and 0.84 mm (`0.28) of the average symmetric contour distance. The statistical analysis results using paired t-test and Bland–Altman plots analysis indicate that the 95% limits of agreement for inter observer variability between the automated measurements and the senior expert measurements are 2.7 mm of BPD, 5.8 mm of OFD, and 10.4 mm of HC, whereas the mean differences are −0.038 ` 1.38 mm, −0.20 ` 2.98 mm, and −0.72 ` 5.36 mm, respectively. These narrow 95% limits of agreements indicate a good level of consistency between the automated and the senior expert’s measurements

A Self-Organising Mixture Network for Density Modelling

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Brain tumour grading in different MRI protocols using SVM on statistical features

In this paper a feasibility study of brain MRI dataset classification, using ROIs which have been segmented either manually or through a superpixel based method in conjunction with statistical pattern recognition methods is presented. In our study, 471 extracted ROIs from 21 Brain MRI datasets are used, in order to establish which features distinguish better between three grading classes. Thirty-eight statistical measurements were collected from the ROIs. We found by using the Leave-One-Out method that the combination of the features from the 1st and 2nd order statistics, achieved high classification accuracy in pair-wise grading comparisons