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A mismatch of a few millimetres is already a big error for image registration in small-animal imaging: e.g. Ji et al. [31] showed that when using a proper calibration method for co-registration and a special bed mounting interface, the accuracy of small-animal SPECT-CT registration can reach sub-millimetre accuracy without using extra markers.
When fusing the complementary information obtained from different imaging modalities, there is a need for image registration to integrate anatomical context with functional image information [24].
It is also used for image registration.
Such large captures both increase speed and reduce the need for image registration (data stitching).
In contrast, much less algorithms for image registration are nowadays available for material applications.
A similar observation has been reported in the literature and found problematic for image registration [20].
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Thomas and Sugimoto [24] proposed to use the reflectance properties for images registration to better perform on featureless images.
In the remainder, only results for these similarity measures (i.e. MI for CT and NCC for other image registration strategies) will be presented.
Therefore, the proposed method can obtain reliable and accurate results for monomodality image registration, which is suitable for monomodality images.
Detection and description of keypoints are very important for better correspondence, and thus for better image registration.
By setting the confidence level for the image registration result (i.e., the threshold for the validation region), we can exclude undesired updates of the pixel values.
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