Image Analysis

Common operations applied to medical images involve various forms of image registration and segmentation. Often, these are considered as ad hoc steps within some form of processing pipeline, with little or no attempt to consolidate or generalise the principles. A more rigorous approach involves formulating all these operations within a unifying probabilistic generative model of the data. Of particular importance to medical imaging is the incorporation of spatial deformations within the model, along with methods for encoding inter-subject variability.  There is still much work to be done in the area of biomedical image analysis, image datasets are large and models are complex, so numerous approximations are necessary - although a continuation of Moore's Law would allow much more to be achieved. In general, those models that most accurately encode real biological phenomena (basic science applications) are likely to be the most useful for making predictions on which real-world medical decisions may be made (applied science). In addition to addressing the immediate short term needs of biologists and clinicians, imaging scientists should also be laying the groundwork for a future in which harnessing `big data' in the NHS will revolutionise healthcare.

Theme Leader: Prof. John Ashburner

Page last modified on 01 dec 13 21:38