Abstract
Registration of multi-modality images requires similarity measures that can deal with complex and unknown image intensity dependencies. Such measures have to rely on statistics, and consequently, they require relatively large image regions to operate. This makes the detection of localized image discrepancies difficult. As a solution we propose point similarity measures, which can measure similarity of arbitrarily small image regions, including similarity of individual image points. In this paper we present a point similarity measure derived from the mutual information. In addition to its extreme locality it can also avoid the interpolation artifacts and improve the spatial regularization to better suit the spatial deformation model.
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Rogelj, P., Kovačič, S. (2003). Point Similarity Measure Based on Mutual Information. 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_12
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DOI: https://doi.org/10.1007/978-3-540-39701-4_12
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