Tissue thickness calculation in ocular optical coherence tomography

Thickness measurements derived from optical coherence tomography (OCT) images of the eye are a fundamental clinical and research metric, since they provide valuable information regarding the eye’s anatomical and physiological characteristics, and can assist in the diagnosis and monitoring of numerous ocular conditions. Despite the importance of these measurements, limited attention has been given to the methods used to estimate thickness in OCT images of the eye. Most current studies employing OCT use an axial thickness metric, but there is evidence that axial thickness measures may be biased by tilt and curvature of the image. In this paper, standard axial thickness calculations are compared with a variety of alternative metrics for estimating tissue thickness. These methods were tested on a data set of wide-field chorio-retinal OCT scans (field of view (FOV) 60° x 25°) to examine their performance across a wide region of interest and to demonstrate the potential effect of curvature of the posterior segment of the eye on the thickness estimates. Similarly, the effect of image tilt was systematically examined with the same range of proposed metrics. The results demonstrate that image tilt and curvature of the posterior segment can affect axial tissue thickness calculations, while alternative metrics, which are not biased by these effects, should be considered. This study demonstrates the need to consider alternative methods to calculate tissue thickness in order to avoid measurement error due to image tilt and curvature.

Thickness metrics in posterior segment optical coherence tomography

To develop and compare a set of metrics for calculating tissue thickness in wide-field OCT data.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Carrie the Musical

When Carrie the Musical first debuted in 1988 it (in)famously closed eight days later, earning the dubious title of being one of the biggest flops in the history of Broadway. This revived version of the work, the Queensland premiere of the production, presented at the Brisbane Powerhouse in January 2016, was a calculated experiment in the commercialisation of an historical flop into a contemporary success. Through collaboration between some of Brisbane's most promising young creatives, designers, choreographers musicians and performers, this production became one of the Brisbane Powerhouse's most successful shows and gained local, national and international recognition for its achievements. By pushing the boundaries of the most current trends in contemporary Australian directing and performance making, the creative team was able to draw on their innovative capacity as independent theatre makers to turn a once-maligned work into a modern-day financial and critical success.