Abstract
Biomedical Imaging has grown significantly for the past twenty years, as it is considered as a unique method for visualizing biological processes within living organisms in a non-invasive manner. Although works in biomedical image analysis rely on underlying biological problems, scientists are just beginning to embrace the idea that these works will benefit from multidisciplinary interactions. Moreover, within the computer vision community, time has come for a more holistic and integrated approach in order to articulate statistical/machine learning and knowledge-based approaches. In this paper we present studies based on these two classic approaches and show how their complementarity may benefit biomedical imaging.
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Cloppet, F., Hurtut, T. (2012). Knowledge Based and Statistical Based Approaches in Biomedical Image Analysis. In: Loménie, N., Racoceanu, D., Gouaillard, A. (eds) Advances in Bio-Imaging: From Physics to Signal Understanding Issues. Advances in Intelligent and Soft Computing, vol 120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25547-2_14
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DOI: https://doi.org/10.1007/978-3-642-25547-2_14
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