Abstract
There is a wide range of segmentation methods for bone structures in CT images. Many of these methods are declared as automatic, but it is not guaranteed, that the resulting segmentation labels the volume of interest correctly in any case. This work presents a technique, which assists the user with the necessary corrections of the segmentation errors. The procedure must be started manually, but the following steps are fully automatic. First, a similar, correct segmentation is selected from a database, which is used to mask the defects. Then the selected segmentation is registered onto the defect one using the diffeomorphic demons algorithm. Thereby, the region inside the mask is excluded from registration but the displacement field is interpolated. The method has been implemented and tested for segmentations of the proximal femur head, but can easily be transferred to segmentations of other bone regions.
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References
Bankman IN. Handbook of Medical Imaging: Processing and Analysis. Burlington: Academic Press; 2009.
Kronman A, Joskowicz L. Image segmentation errors correction by mesh segmentation and deformation. Med Image Comput Comput Assist Interv. 2013;16:206–13.
Henn S, Hoemke L, Witsch K. A generalized image registration framework using incomplete image information - with application to lesion mapping. In: Mathematics in Industry. vol. 10. Springer; 2006. p. 3–25.
Lamecker H, Pennec X. Atlas to image-with-tumor registration based on demons and deformation inpainting. Proc MICCAI. 2010.
Thirion JP. Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal. 1998;2:243–60.
Press WH, et al. Numerical Recipes. New York: Cambridge University Press; 2007.
Vercauteren T, et al. Diffeomorphic demons: efficient non-parametric image registration. NeuroImage. 2009;45:61–72.
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© 2015 Springer-Verlag Berlin Heidelberg
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Friedberger, A., Museyko, O., Engelke, K. (2015). Binary Image Inpainting with Interpolation-Enhanced Diffeomorphic Demons Registration. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_19
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DOI: https://doi.org/10.1007/978-3-662-46224-9_19
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