Dynamic shape modelling in reconstructive surgery


Contact Prof K.V. Mardia, Prof J.T. Kent and Prof B.S. Khambey to discuss this project further informally.

Project description

Objects are everywhere, natural and man-made. Advances in technology have led to the routine collection of geometrical information on objects and the study of their shape is more important than ever. Analytically, shape comprises the geometrical information that remains when location, scale and rotational effects are removed from the description of an object.

In many settings it is possible to define "landmarks" which can be consistently identified across a set of objects, and developments over the past 30 years have led to the new subject of statistical shape analysis, an extension of multivariate analysis. We have pioneered the development of statistical shape methodology for many medical, biological and computational real examples. More details can be found in Dryden, I.L. and Mardia, K.V. (2016) Statistical Shape Analysis with Applications in R, second edition, Wiley. In dynamic shape analysis the shape of an object changes through time.

The time scale can range from years, e.g. the growth of a human face between childhood and adulthood, down to seconds, e.g. the formation of human facial expressions such as a smile. Such applications can be viewed as multivariate time series, sometimes with change points. It has some methodological connection with our pioneering contribution to growth assessment through facial LASER scans of children.

The motivating application for this project comes from the use of craniofacial surgery to correct facial deformities such as a cleft lip. One measure of success for the surgery is the ability of the patient to make "normal-looking" facial expressions, such as smile. At the moment this judgement is made subjectively. Using a 3D motion capture camera system, we have already acquired some dynamic data for the normal human smile.

The aim of the project is to develop and assess more objective measures to quantify the success of surgery, leading to a high impact contribution.

Entry requirements

Applications are invited from candidates with or expecting a minimum of a UK upper second class honours degree (2:1), and/or a Master's degree in a relevant degree such as (but not limited to) statistics or mathematics.

If English is not your first language, you must provide evidence that you meet the University's minimum English Language requirements.

How to apply

Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is 'Dynamic shape modelling in reconstructive surgery' as well as Professor Kanti Mardia and Professor John Kent as your proposed supervisors.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.

If you require any further information please contact the Graduate School Office, e: maps.pgr.admissions@leeds.ac.uk