Abstract
An accurate, computationally efficient and fully-automated algorithm for the alignment of 2D serially acquired sections forming a 3D volume is presented. The method accounts for the main shortcomings of 3D image alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices, missing slices. The approach relies on the optimization of a global energy function, based on the object shape, measuring the similarity between a slice and its neighborhood in the 3D volume. Slice similarity is computed using the distance transform measure in both directions. No particular direction is privileged in the method avoiding global offsets, biases in the estimation and error propagation. The method was evaluated on real images (medical, biological and other CT scanned 3D data) and the experimental results demonstrated the method’s accuracy as reconstruction errors are less than 1 degree in rotation and less than 1 pixel in translation.
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Krinidis, S., Nikou, C., Pitas, I. (2002). 3D Volume Reconstruction by Serially Acquired 2D Slices Using a Distance Transform-Based Global Cost Function. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_35
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DOI: https://doi.org/10.1007/3-540-46014-4_35
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