@InProceedings{sofka:miccai08,
  author =	 {Michal Sofka and Charles V.\ Stewart},
  title =	 {Location Registration and Recognition ({LRR}) for
                  Longitudinal Evaluation of Corresponding Regions in
                  {CT} Volumes},
  booktitle =    {Proceedings of the 11th International Conference on
                  Medical Image Computing and Computer-Assisted
                  Intervention (MICCAI 2008)},
  year =         2008,
  month =        {6--10~} # sep,
  address =      {New York, NY},                  
  pages =	 {989--997},
  volume =	 2,
  abstract =	 {The algorithm described in this paper takes (a) two
                  temporally-separated CT scans, I1 and I2, and (b) a
                  series of locations in I1, and it produces, for each
                  location, an affine transformation mapping the
                  locations and their immediate neighborhood from I1
                  to I2. It does this without deformable registration
                  by using a combination of feature extraction,
                  indexing, refinement and decision
                  processes. Together these essentially
                  ``recognize'' the neighborhoods. We show on lung
                  CT scans that this works at near interactive speeds,
                  and is at least as accurate as the Diffeomorphic
                  Demons algorithm. The algorithm may be used both for
                  diagnosis and treatment monitoring.},
  url =		 {http://www.vision.cs.rpi.edu/LRR/}
}