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Biomedical time series signal and image de-noising and classification

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The purpose of the work is to develop novel signal processing tools to perform biomedical time series analysis and classification (ECG, EMG, EEG, MRI or hyperspectral images). The candidate will also further develop these techniques for de-noising individual pixels/voxels in images to enable more reliable segmentation. Robust thresholding criteria will be adopted to adaptively de-noise these datasets. Image classification will then be performed using neural network or support vector machine architectures. Deep learning techniques will also be considered. This is a software based project only, focusing on chemometric analysis and algorithm development performed using MATLAB/Python software. The successful candidate once he/she acquires the relevant skills is expected to also liaise and collaborate with software developers at the Oxford based company Brainomix which pioneer the e-ASPECTS software solution. An overview of some of the relevant approaches to be adopted can be found in my recent book: Yin X., Hadjiloucas, S. and Zhang, Y. (2017) Pattern classification of medical images: computer aided diagnosis. Springer, ISBN 9783319570266

Funding Notes

Eligibility requirements: First class / upper second OR lower second with a Masters Degree in a relevant discipline


See my website for relevant publications. You may contact me directly at: [email protected]

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