The Robotics Institute
Search the site
RI | Publications | Probabilistic Registration of 3-D Medical Images

Text only version of this site

Probabilistic Registration of 3-D Medical Images
M. Chen, T. Kanade, D. Pomerleau, and J. Schneider
tech. report CMU-RI-TR-99-16, Robotics Institute, Carnegie Mellon University, July, 1999.

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

Download [Help]

Adobe portable document format (pdf) [538 KB]
Compressed postscript (ps.gz) [1644 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.

Abstract

Registration between 3-D images of human anatomies enables cross-subject diagnosis. However, innate differences in the appearance and location of anatomical structures between individuals make accurate registration difficult. We characterize such anatomical variations to achieve accurate registration.

We represent anatomical variations in the form of statistical models, and embed these statistics into a 3-D digital brain atlas which we use as a reference. These models are built by registering a training set of brain MRI volumes with the atlas. This associates each voxel in the atlas with multi-dimensional distributions of variations in intensity and geometry of the training set. We evaluate statistical properties of these distributions to build a statistical atlas. When we register the statistical atlas with a particular subject, the embedded statistics function as prior knowledge to guide the deformation process. This allows the deformation to tolerate variations between individuals while retaining discrimination between different structures. This method gives an overall voxel mis-classification rate of 2.9% on 40 test cases; this is a 34% error reduction over the performance of our previous algorithm without using anatomical knowledge. Besides achieving accurate registration, statistical models of anatomical variations also enable quantitative study of anatomical differences between populations.

Notes

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

Note: The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government.

Text Reference

M. Chen, T. Kanade, D. Pomerleau, and J. Schneider, Probabilistic Registration of 3-D Medical Images, tech. report CMU-RI-TR-99-16, Robotics Institute, Carnegie Mellon University, July, 1999.

BibTeX Reference

@techreport{Chen_1999_2564,
   author = "Mei Chen and Takeo Kanade and Dean Pomerleau and Jeff Schneider",
   title = "Probabilistic Registration of 3-D Medical Images",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "July",
   year = "1999",
   number = "CMU-RI-TR-99-16",
   address = "Pittsburgh, PA",
   note = "The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. government."
}


The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.
For updates and comments, please see these instructions.
This page maintained by robotwebmaster@ri.cmu.edu