Statistical Image Analysis for a Confocal Microscopy 2D Section of Cartilage Growth

Images are the source of information in many areas of scientific enquiry. A common objective in these applications is reconstruction of the true scene from a degraded image. When objects in the image can be described parametrically, reconstruction can proceed by fitting a high level image model. In this article we consider the analysis of confocal fluorescence microscopeimages of cells in an area of cartilage growth. Biological questions posed by the experimenters concern the nature of the cells in the image and changes in their properties with time. Our model of the imaging process is based on a detailed analysis of the data. We treat the true scene as a realisation of a marked point process, incorporating this as the high-level prior model in a Bayesian analysis. Inference is by simulation using reversible jumpversions of Markov chain Monte Carlo (MCMC) algorithms which can handle the varying dimension of the image description arising

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