Abstract
We have developed a general purpose registration algorithm for medical images and volumes. The transformation between images is modelled as locally affine but globally smooth, and explicitly accounts for local and global variations in image intensities. An explicit model of missing data is also incorporated, allowing us to simultaneously segment and register images with partial or missing data. The algorithm is built upon a differential multiscale framework and incorporates the expectation maximization algorithm. We show that this approach is highly effective in registering a range of synthetic and clinical medical images.
This work was supported by an Alfred P. Sloan Fellowship, a NSF CAREER Award (IIS-99-83806), and a department NSF infrastructure grant (EIA-98-02068). The authors can be reached at sp@cs.dartmouth.edu and farid@cs.dartmouth.edu.
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Periaswamy, S., Farid, H. (2003). Elastic Registration with Partial Data. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds) Biomedical Image Registration. WBIR 2003. Lecture Notes in Computer Science, vol 2717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39701-4_11
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DOI: https://doi.org/10.1007/978-3-540-39701-4_11
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