Abstract
A quantitative assessment of mapping accuracy based on mutual information index (MI), calculated from gray scale 2D histogram, is applied to an automated multimodality (un)warping algorithm. Information rich histological image data, which present non-linear deformations due to the specimen sectioning, is reconstituted into deformation-corrected, 3D volumes for geometric mapping to anatomical data for spatial analyses. Thin-plate-spline (TPS) algorithm has been implemented for automatic unwarping of the distortions using a multivariate optimizer and MI as a global cost function. The MI proves to be a robust objective matching criterion effective for automatic multimodality warping for 2D data sets and can be readily applied to volumetric 3D registrations. The improved performance of TPS warping compared to full affine transformation is quantified by comparison of MI's of both methods.
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© 1996 Springer-Verlag Berlin Heidelberg
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Kim, B., Boes, J.L., Frey, K.A., Meyer, C.R. (1996). Mutual information for automated multimodal image warping. In: Höhne, K.H., Kikinis, R. (eds) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol 1131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0046973
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DOI: https://doi.org/10.1007/BFb0046973
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