Abstract
It is difficult to directly co-register the 3D FMT (Fluorescence Molecular Tomography) image of a small tumor in a mouse whose maximal diameter is only a few mm with a larger CT image of the entire animal that spans about ten cm. This paper proposes a new method to register 2D flat and projected CT image first to facilitate the registration between small 3D FMT images and large CT images. And a novel algorithm Bayesian Iterative Closest Point (BICP) is introduced and validated in 2D affine registration. The visualization of the alignment of the 3D FMT and CT image through 2D registration shows promising results that would lead to automated 3D registration.
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© 2007 Springer Berlin Heidelberg
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Zheng, X., Zhou, X., Sun, Y., Wong, S.T.C. (2007). Registration of 3D FMT and CT Images of Mouse Via Affine Transformation with Bayesian Iterative Closest Points. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_135
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DOI: https://doi.org/10.1007/978-3-540-72393-6_135
Publisher Name: Springer, Berlin, Heidelberg
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