The primary research interests within the program are in applying imaging and signal processing methodologies, including pattern recognition and image registration, to problems within the medical and life sciences. In particular, we are interested in extracting the maximum diagnostic and prognostic information from clinical images. Currently we are actively involved in projects on:
- The texture of clinical images. The potential of quantitative texture measures to discriminate between normal and abnormal status in medical imaging has long been recognized. I have investigated the clinical applicability of a number of computationally tractable texture indices and have identified fractal signature and lacunarity as the most promising discriminators for diagnosing compromised bone quality in osteoporosis. The relationship between lacunarity and fractal dimension, the applicability of grayscale lacunarity, and the clinical sensitivity of the indices are being investigated further.
- Tortuosity (of blood vessels, and the spine). Increasing vessel curvature or tortuosity with age may be a risk factor in the development of atherosclerosis: I am investigating the quantitative measurement of arterial tortuosity using CT and MRI images, based on the minimum curvature of approximating piece-wise splines to the mid-line. I am keen to investigate its validity as an indicator of changes in morphology by applying it to a variety of vascular systems (including retinal blood vessels) and to the curvature of the spine (scoliosis).
- The curvature of the universe. We are exploring the feasibility of using lacunarity analysis as a new method of determining the Friedmann curvature of the Universe using theoretical cosmological models and observational redshift data from the Sloan Digital Sky Survey. The immediate impact will be to provide the astrophysics community with an alternative measure to fractal analysis of the nature and extent of the large scale distribution of matter.
- Biometrics. The application of pattern recognition strategies to fingerprint and face recognition.
- Helix-coil transition in peptides. Peptides are short biopolymers which undergo a thermal phase transition from an ordered (helical) state to a disordered random coil state. A nonadditive statistical mechanical model is employed to study this phase transition and shows transferability to different experimental conditions.
- Water. A nonadditive statistical mechanical model of water, consistent with physical experimental data, is developed for use with high speed molecular dynamics simulations of large biomolecules.
- Spin glass dynamics. Spin glasses are frustrated and disordered magnetic systems with complex time dependent behavior. A hierarchical barrier model of spin glass dynamics is used to develop a slow growing time and temperature dependent correlation length.