Computational Modelling Group

Medical Imaging

Medical imaging plays a vital role in analysing, modelling and understanding biological systems. Technologies such as computed tomography (CT), magnetic resonance imaging (MRI), and a wide range of associated techniques (DEXA, RSA, etc) play a major role in clinical diagnosis and planning. Researchers at Southampton use medical imagining techniques and data for a wide range of modelling purposes. With the computational modelling group, researchers are concerned with the challenges of processing this large volume of medical imaging data, filtering and sampling to handle data artifacts, and processing to extract useful information from these data sets.

For queries about this topic, contact Anthony Strickland.

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Projects

Centre for Doctoral Training in Next Generation Computational Modelling

Hans Fangohr, Ian Hawke, Peter Horak (Investigators), Susanne Ufermann Fangohr, Thorsten Wittemeier, Kieran Selvon, Alvaro Perez-Diaz, David Lusher, Ashley Setter, Emanuele Zappia, Hossam Ragheb, Ryan Pepper, Stephen Gow, Jan Kamenik, Paul Chambers, Robert Entwistle, Rory Brown, Joshua Greenhalgh, James Harrison, Jonathon Waters, Ioannis Begleris, Craig Rafter

The £10million Centre for Doctoral Training was launched in November 2013 and is jointly funded by EPSRC, the University of Southampton, and its partners.

The NGCM brings together world-class simulation modelling research activities from across the University of Southampton and hosts a 4-year doctoral training programme that is the first of its kind in the UK.

MXL Project

Mark Taylor, Junfen Shi (Investigators)

‘MXL’ is short for “Enhanced patient safety by computational Modelling from clinically available X-rays to minimise the risk of overload and instability for optimised function and Longevity”. This is an international EU-funded project which the Bioengineering Sciences Research Group at Southampton is involved in. For more information, visit http://www.m-x-l.eu

Prediction of Psychopathology by MRT data

We aim to predict psychopathological outcomes in adults by functional brain data using multilevel regression and crossvaligdation strategies.

People

Hans Fangohr
Professor, Engineering Sciences (FEE)
Mark Taylor
Professor, Engineering Sciences (FEE)
Peter Horak
Reader, Optoelectronics Research Centre
Thomas Blumensath
Senior Lecturer, Institute of Sound & Vibration Research (FEE)
Ian Hawke
Lecturer, Mathematics (FSHS)
Aravinthan Varatharaj
Research Fellow, Medicine (FM)
Ioannis Begleris
Postgraduate Research Student, Engineering Sciences (FEE)
Rory Brown
Postgraduate Research Student, Civil Engineering & the Environment (FEE)
Paul Chambers
Postgraduate Research Student, Engineering Sciences (FEE)
Caroline Duignan
Postgraduate Research Student, Biological Sciences (FNES)
Robert Entwistle
Postgraduate Research Student, Engineering Sciences (FEE)
Stephen Gow
Postgraduate Research Student, Engineering Sciences (FEE)
Joshua Greenhalgh
Postgraduate Research Student, Engineering Sciences (FEE)
James Harrison
Postgraduate Research Student, Engineering Sciences (FEE)
David Lusher
Postgraduate Research Student, Engineering Sciences (FEE)
Alvaro Perez-Diaz
Postgraduate Research Student, Engineering Sciences (FEE)
Craig Rafter
Postgraduate Research Student, Engineering Sciences (FEE)
Hossam Ragheb
Postgraduate Research Student, Engineering Sciences (FEE)
Kieran Selvon
Postgraduate Research Student, Engineering Sciences (FEE)
Ashley Setter
Postgraduate Research Student, Engineering Sciences (FEE)
Pegah Tayaranian Hosseini
Postgraduate Research Student, Institute of Sound & Vibration Research (FEE)
Jonathon Waters
Postgraduate Research Student, Engineering Sciences (FEE)
Thorsten Wittemeier
Postgraduate Research Student, Engineering Sciences (FEE)
Emanuele Zappia
Postgraduate Research Student, Engineering Sciences (FEE)
Matthew Higgins
Undergraduate Research Student, Biological Sciences (FNES)
Susanne Ufermann Fangohr
Administrative Staff, Civil Engineering & the Environment (FEE)
Jan Kamenik
Alumnus, University of Southampton
Junfen Shi
None, None