Distance metric learning for medical image registration

Medical image registration has received considerable attention in medical imaging and computer vision, because of the large variety of ways in which it can impact patient care. Over the years, many algorithms have been proposed for medical image registration. Medical image registration uses techniques to create images of parts of the human body for clinical purposes. This thesis focuses on one small subset of registration algorithms: using machine learning techniques to train the similarity measure for use in medical image registration. This thesis is organized in the following manner. In Chapter 1 we introduce the idea of image registration, describe some some applications in medical imaging, and mathematically formulate the three main components of any registration problem: geometric transformation, similarity measure and optimization procedure. Finally we describe how the ideas in this thesis t into the eld of medical image registration, and we describe some related work. In Chapter 2 we introduce the concept of machine learning and we provide examples to illustrate machine learning algorithms. We then describe the knn-nearest neighbors algorithm and the relationship between Euclidean and Mahalanobis distance. Next we introduce distance metric learning and present two approaches for learning the Mahalanobis distance. Finally we provide a description and visual comparison of two algorithms for distance metric learning. In Chapter 3 we describe how distance metric learning can be applied to the problem of medical image registration. Our goal is to learn the optimal similarity measure given a training dataset of correctly registered images. To assess the performance of the two distance metric learning algorithms we test them using images from a series of patients. Moreover we illustrate the sensitivity of one of the learning algorithms by examining the variability of the resulting target registration errors. Finally we present our experimental results of registering CT and MR images. Finally in Chapter 4 we suggest some ideas for future work in order to improve our registration results and to speed up the algorithms

A 'metamorphosis of perspectives on the past': A Study of the Hyde Park Barracks, 1975-2012.

This thesis is an investigation of the site history of the Hyde Park Barracks since 1975 when the decision was made to restore the historical fabric of the building for use as a museum. Paramount to this enquiry is the understanding that historic buildings are always in a process of change, both physically and conceptually. This study will argue that a ‘metamorphosis of perspectives on the past’ at this site has been shaped by cultural, political and economic development over time and a quest of modern identity

Emulation of random output simulators

Computer models, or simulators, are widely used in a range of scientific fields to aid understanding of the processes involved and make predictions. Such simulators are often computationally demanding and are thus not amenable to statistical analysis. Emulators provide a statistical approximation, or surrogate, for the simulators accounting for the additional approximation uncertainty. This thesis develops a novel sequential screening method to reduce the set of simulator variables considered during emulation. This screening method is shown to require fewer simulator evaluations than existing approaches. Utilising the lower dimensional active variable set simplifies subsequent emulation analysis. For random output, or stochastic, simulators the output dispersion, and thus variance, is typically a function of the inputs. This work extends the emulator framework to account for such heteroscedasticity by constructing two new heteroscedastic Gaussian process representations and proposes an experimental design technique to optimally learn the model parameters. The design criterion is an extension of Fisher information to heteroscedastic variance models. Replicated observations are efficiently handled in both the design and model inference stages. Through a series of simulation experiments on both synthetic and real world simulators, the emulators inferred on optimal designs with replicated observations are shown to outperform equivalent models inferred on space-filling replicate-free designs in terms of both model parameter uncertainty and predictive variance

Managing uncertainty in complex stochastic models: design and emulation of a Rabies model

In this paper we present a novel method for emulating a stochastic, or random output, computer model and show its application to a complex rabies model. The method is evaluated both in terms of accuracy and computational efficiency on synthetic data and the rabies model. We address the issue of experimental design and provide empirical evidence on the effectiveness of utilizing replicate model evaluations compared to a space-filling design. We employ the Mahalanobis error measure to validate the heteroscedastic Gaussian process based emulator predictions for both the mean and (co)variance. The emulator allows efficient screening to identify important model inputs and better understanding of the complex behaviour of the rabies model