Zusammenfassung
The identification of one-to-one point correspondences between image objects is one key aspect and at the same time the most challenging part of generating statistical shape and appearance models. Using probabilistic correspondences between samples instead of accurately placed landmarks for shape models [1] eliminated the need of extensive and time consuming landmark and correspondence determination, and furthermore, the dependency of the quality of the generated model on potentially wrong correspondences was reduced.
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Literatur
Hufnagel H, Pennec X, Ehrhardt J, et al. Generation of a statistical shape model with probabilistic point correspondences and the expectation maximization-iterative closest point algorithm. IJCARS. 2008;2:265–273.
Krüger J, Ehrhardt J, Handels H. Statistical appearance models based on probabilistic correspondences. Med Image Anal. 2017;37:146–159.
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Krüger, J., Ehrhardt, J., Handels, H. (2018). Abstract: Probabilistic Appearance Models for Medical Image Analysis. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_23
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DOI: https://doi.org/10.1007/978-3-662-56537-7_23
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