Abstract
Current approaches to spatial normalisation of brain images have made use of a target image to which each subject image is matched. However, in many cases the use of a single brain template, or a statistical one derived from multiple subjects of another population, does not adequately capture the structure present in a population of anatomies under investigation. In such cases this paper proposes that a better approach may be to seek a method of driving subjects in the group into registration with each other, rather than with an unrepresentative template. This paper explores the approach of extending registration concepts from multi-modality registration, specifically those deriving criteria from the joint probability distribution of image values, to the general case of describing the alignment of a population of images simultaneously. Geometric constraints forcing the convergence to an average geometric shape are discussed and results presented on synthetic images and clinical brain image data.
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Studholme, C. (2003). Simultaneous Population Based Image Alignment for Template Free Spatial Normalisation of Brain Anatomy. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds) Biomedical Image Registration. WBIR 2003. Lecture Notes in Computer Science, vol 2717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39701-4_9
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DOI: https://doi.org/10.1007/978-3-540-39701-4_9
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