Graphics enhanced version of this site

3-D Deformable Registration of Medical Images Using a Statistical Atlas
M. Chen, T. Kanade, D. Pomerleau, and J. Schneider
tech. report CMU-RI-TR-98-35, Robotics Institute, Carnegie Mellon University, December, 1998.

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Adobe portable document format (pdf) [373 KB]
Compressed postscript (ps.gz) [958 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Registration between voxel images of human anatomy enables cross-patient diagnosis and post-treatment analysis. However, innate variations in the shape, size, and density of non-pathological anatomical struc-tures between individuals make accurate registration difficult. Characterization of such normal but inherent variations provides guidance for registration.We extracted the pattern of normal variations in the appear-ances of brain structures from the T1-weighted magnetic resonance imaging (MRI) volumes of 105 sub-jects. This knowledge serves as a domain-relevant constraint which increases the accuracy of deformable registration.

We represent domain knowledge in the form of voxel statistics, and embed these statistics into a 3-D digital brain atlas which we use as the reference. The knowledge acquisition process involves registering a training set of MRI volumes with the atlas. The method employed is a previously developed 3-D hierarchical deformable registration algorithm. This associates each voxel in the reference atlas with distributions of nor-mal variations of intensity and 3-D positions of the training set. We evaluate statistical properties of these distributions for each atlas voxel to build a statistical atlas which contains the anatomical information of the population. When we register this atlas to a particular subject, the embodied statistics function as domain-relevant constraints. The deformation process tolerates non-pathological variations between subjects. When applied to 40 test cases, this knowledge-constrained registration method achieved a correct voxel classifica-tion rate above 95% for 36 cases; this is a 24% improvement over the performance of the algorithm without knowledge constraints.

To overcome imprecisions in unconstrained registration that affect the rigorousness of the statistical atlas, we propose to build an initial statistical model of a small but accurately registered training set, then boot-strap it into a more reliable model. Besides guiding deformable registration, our knowledge representation also enables quantitative investigation of possible anatomical divergences between populations.

Associated centers: VASC and MRTC
Associated project: Knowledge-Guided Deformable Registration

M. Chen, T. Kanade, D. Pomerleau, and J. Schneider, 3-D Deformable Registration of Medical Images Using a Statistical Atlas, tech. report CMU-RI-TR-98-35, Robotics Institute, Carnegie Mellon University, December, 1998.

@techreport{Chen_1998_528,
   author = "Mei Chen and Takeo Kanade and Dean Pomerleau and Jeff Schneider",
   title = "3-D Deformable Registration of Medical Images Using a Statistical Atlas",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "December",
   year = "1998",
   number = "CMU-RI-TR-98-35",
   address = "Pittsburgh, PA"
}