You are here

Student projects

Below follows a list of project descriptions for students. Some of the projects are finished, some are in progress, and some are still available to students that want to do a UG4, MSc or a PhD projects.

If you want to do an MSc or PhD with us then you need to go through the application procedures set by the School of Informatics. Make sure you discuss your research proposal first with Malcolm Atkinson or David Robertson. Important to note: you need to apply under Intelligent Systems & their Applications.

List of projects

A quantitative diagnostic method incorporating brain images and clinical measures

Student: 
Grade: 

This is a project in collaboration with the Brain Research Imaging Centre under the
Edinburgh Imaging Prize Studentships (Centre for In Vivo Imaging Science).
See http://www.edinburghimaging.com/studentships/advertising.html
and the form in http://www.edinburghimaging.com/documents/CIVIS%20PhDOct2013/application...

Description:

Project status: 
Still available
Degree level: 
PhD
Background: 
The project would suit a student with strong statistical skills and a background in Neuroinformatics, Neuroscience, or Psychology but also potentially a student from a pure Statistical, Mathematical, or Engineering background and an interest in brain ageing and pathology. It requires understanding of statistical analyses and summaries (e.g. hypotheses testing, means, and percentiles), clinical and brain image data, the sensitivities and management of these data; and the ability to work as part of an interdisciplinary group of researchers. There will be additional mentorship from Prof Joanna Wardlaw (expertise in neuroimaging).
Supervisors @ NeSC: 
Other supervisors: 
Dr Dominic Job Dr Susan Shenkin
Subject areas: 
References: 
1. Dickie, D.A., et al. (2012). Do brain image databanks support understanding of normal ageing brain structure? A systematic review. Eur Radiol. 22, 1385-1394. 2. Farrell, C., et al. (2009). Development and initial testing of normal reference MR images for the brain at ages 65–70 and 75–80 years. Eur Radiol. 19, 177–183. 3. Mazziotta, J.C., et al. (2009). The myth of the normal, average human brain - the ICBM experience: (1) Subject screening and eligibility. Neuroimage. 44, 914-922. 4. Freedman, D. (2010). Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press, Cambridge. 5. Breteler, M., et al. (1994). Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study. Neurology. 44,1246-1252.
Student project type: 

Distributed multi-modal image collection and analysis

Multimodal Image data banks, of normal [3] and pathological subjects, are of great utility for improving collaboration and performing research with greater statistical power. The acquisition of images is expensive and time consuming; therefore it is important to reuse them. We are currently developing human brain image data banks, one of which is likely to be the largest bank of normal aging brains in the world.

Project status: 
Still available
Degree level: 
PhD
Background: 
Computer Science, mathematical sciences or engineering with strong foundations in computing
Supervisors @ NeSC: 
Other supervisors: 
Dominic E. Job
Subject areas: 
Bioinformatics
References: 
1. Slomka P, Baum, R. Multimodality image registration with software: state-of-the-art. EJNMMI. 36 2. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroimform. 5:13 3. Dickie, D.A., Job, D.E., Poole, I., Ahearn, T.S., Staff, R.T., Murray, A.D., Wardlaw, J.M., 2012. Do brain image databanks support understanding of normal ageing brain structure? A systematic review. Eur. Radiol. 22, 1385-1394.
Student project type: 

Early detection of infarcts by improving brain perfusion imaging analysis

Outcome after severe ischemic stroke may improve with thrombolysis. Some studies have shown that parametric perfusion maps and other information might be useful in selecting patients for this potentially hazardous treatment. Traditionally, perfusion source images are deconvolved in order to create these parametric maps; such as cerebral blood flow and volume [1].

Project status: 
Still available
Degree level: 
PhD
Background: 
Informatics, Neuroscience, statistics or Mathematics.
Supervisors @ NeSC: 
Other supervisors: 
Joanna.Wardlaw
Subject areas: 
Bioinformatics
References: 
1. L. Ostergaard, R. Weisskoff, D. Chesler, C. Gyldensted, and B. Rosen, “High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. part i: Mathematical approach and statistical analysis,” Magn Reson Med, vol. 36, no. 5, pp. 715–25, 1996. 2. F. Zhu, T. Carpenter, D. R. Gonzalez, M. Atkinson, and J. Wardlaw, “Computed tomography perfusion imaging denoising using Gaussian process regression,” Physics in Medicine and Biology, vol. 57, no. 12, pp. N183–198, 2012. 3. F. Zhu, D. R. Gonzalez, T. Carpenter, M. Atkinson, and J. Wardlaw, “Automatic Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging”, IEEE Transactions on Information Technology in Biomedicine, Under Revision. 4. F. Zhu, D. Gonzalez, T. Carpenter, M. Atkinson, and J. Wardlaw, “Parallel per- fusion imaging processing using GPGPU,” Computer Methods and Programs in Biomedicine, 2012. 5. I. Kane, T. Carpenter, F. Chappell, C. Rivers, P. Armitage, P. Sandercock and J. Wardlaw, “Comparison of 10 different magnetic resonance perfusion imaging processing methods in acute ischemic stroke effect on lesion size, proportion of patients with diffusion/perfusion mismatch, clinical scores, and radiologic outcomes”, Am Heart Assoc, vol . 38, no. 12, pp 3158-64, 2007.
Student project type: 

Privacy Protection for a Brain Imaging Databank

Student: 
Jyothsna Vivekanand Shenoy

In recent years there has been an increasing trend towards releasing micro-data to the public. This can be very important for research, but in some cases (e.g. medical data) these releases are limited due to privacy protection issues. Anonymisation is a limited solution that does not fully protect the individuals. Even when all the personal identifiers have been removed it might be possible to identify an individual from an anonymous records using quasi-identifiers and data linking with some other external data source (see references).

Project status: 
Finished
Degree level: 
MSc
Background: 
Knowledge of databases. Programming skills.
Supervisors @ NeSC: 
Student project type: 
References: 
Fung, Benjamin C. M. and Wang, Ke and Chen, Rui and Yu, Philip S. "Privacy-preserving data publishing: A survey of recent developments" ACM Computing Surveys, Vol. 42, No. 4, Article 14 B.-C. Chen, D. Kifer, K. LeFevre and A. Machanavajjhala. "Privacy-Preserving Data Publishing" Foundations and TrendsR in Databases Vol. 2, Nos. 1–2 (2009) 1–167 L. Sweeney. "k-Anonymity: a model for protecting privacy". In International Journal on Uncertainty, Fuzziness and Knowledgebased Systems, 10(5), pages 557-570, 2002 Samarati P (2001). "Protecting respondents' identities in microdata release". IEEE Transactions on Knowledge and Data Engineering, 13(6):1010{1027

Investigating the Rule Construction Mechanism in Ant-Miner

Student: 
Hariharan Anantharaman

This project will appeal to you if you are interested in Learning from Data and Nature-Inspired Computation.

Project status: 
Finished
Degree level: 
MSc
Supervisors @ NeSC: 
Student project type: 

Investigating Array Databases for Managing Climate Data

Student: 
Jian Qiang

This is a challenging project and will appeal to students keen to make a contribution in the areas of scientific databases and geoinformatics.

Project status: 
Finished
Degree level: 
MSc
Subject areas: 
Databases
Software Engineering
Student project type: 

De-identification of faces in 2D DICOM images

With the increasing resolution of MR and CT scans, it has become feasible to reconstruct detailed 3D images of faces.

Usually face de-identification in medical imaging is done after the reconstruction, i.e. in 3D (see references). Different techniques are used to this end including brain extraction, removal of facial features and deformation of the face surface.

Project status: 
Still available
Degree level: 
MSc
Supervisors @ NeSC: 
Other supervisors: 
Trevor Carpenter
Subject areas: 
Machine Learning/Neural Networks/Connectionist Computing
Student project type: 

Scientific applications: exploiting the data bonanza. The microscopy case.

he aim of the project is to perform some exploratory work on how to deal with the problem of I/O bound processing, by implementing technology-specific components in a provided system. The goal is to distribute data and processing so that a CPU processes data locally, minimising data transfer. The assumption is that I/O is the major bottleneck in processing, and computation could be done with less powerful (greener and cheaper) CPUs, rather than with a powerful CPU that wastes energy waiting for data. Different technologies for storing and processing the data can be explored.

Project status: 
Finished
Degree level: 
MSc
Supervisors @ NeSC: 
Subject areas: 
e-Science
Databases
Distributed Systems
Software Engineering
Student project type: 

Computing the best answer you can afford

We are building a data-intensive machine as a research platform to explore data-intensive computational strategies. We are interested in computations over large bodies of data, where the data-handling is a dominant issue. Computational challenges with these properties are getting ever more prevalent as the cost of digital sensors and computational/societal data sources become ever cheaper, ever more powerful and more ubiquitous. The use of algorithms over such data are of growing importance in medicine, planning, engineering, policy and science.

Project status: 
Still available
Degree level: 
MSc
Supervisors @ NeSC: 
Subject areas: 
e-Science
Algorithm Design
Student project type: 

Ad hoc Cloud Computing

Student: 
Gary McGilvary

Commercial cloud providers offer computational services via co-located machines within data centres, whereas private clouds typically offer services via a set of dedicated servers. While both cloud models appeal to the mass market, there exists a long tail of potential cloud users that are unable to take advantage of either public or private cloud computing.

Project status: 
In progress
Degree level: 
PhD
Supervisors @ NeSC: 
Other supervisors: 
Dr. Adam Barker Dr. Ashley Lloyd
Student project type: 

Pages