analysis

Quantiative Calcium Imaging with Computational Optics

Working with the Imaging Concepts Group (University of Glasgow) I am interested in comparing how different computational imaging approaches to calcium imaging effect the analysis of calcium signalling between neurons.

Vessel Enhancement

Working with Cigdem Sazak and Boguslaw Obara (Durham University) I developed a new approach for the enhancement of vessel-like objects in biological and medical imaging called the bowler-hat transform. We have used this approach in the analysis of fungal and slime mould networks, with our collaborator Mark Fricker (Oxford University), retinal imaging and brain vasculature. My contribution to this work was funded by the EPSRC. The key output of this work was published in Pattern Recognition.

Graph Theory of Networks of Neurons

As an EPSRC Doctoral Prize Research Fellow (University of Glasgow), I am investigated the use of graph theory, the mathematical background to network science, to understand how networks of neurons change organisation throughout development. In particular, I am developing analysis pipelines for the quantitative exploration of retinal development as measured by multi-electrode array (MEA) recordings and calcium imaging.

Automated Nuclei Detection

At Durham University, my PhD research focussed on the use of mathematical morphology to quantify biological and medical images. One such challenge was the automated detection of ellipse-like objects, such as cell nulcei, in fluorescence microscopy. Working with Philip Jackson and Boguslaw Obara, I developed a new approach to identifying ellipses based on Hilbert-edge detection and ranging (HEDAR) and assumptions based on the shape on an ellipse. My contribution to this work was funded by EPSRC.

Object-based Colocalisation

Working with researchers at Durham University, the University of Essex and the University of Exeter, I published an article focussing on the benefits of using object-based approaches to quantify colocalisation experiments. The article highlighted not only the benefits of such an approach but the importance of understanding the underlying algorithms and their assumptions by showing the biases and uncertainties of the approach. My contribution to this work was funded by EPSRC.