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A Statistical Quantification of Human Brain Asymmetry
Head: Yanxi Liu

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

Associated centers: VASC and MRTC
Associated labs/groups: Biomedical Image Analysis, Computational Symmetry, and Medical Robotics and Computer Assisted Surgery

For more information, see this project's homepage.

This page last updated - February 2007.

Existing "content-based" image retrieval systems depend on general visual properties such as color and texture to classify diverse, two-dimensional (2D) images. These general visual cues, however, often fail to be effective discriminators for image sets taken within a single domain, where images have subtle, domain-specific differences. Furthermore, these visual properties are not necessarily the true content of an image, nor do they have a proven correspondence to image semantics, i.e. the meaning of an image. Databases composed of (3D volumetric or 2D) images and their collateral information in a particular medical domain form simple, semantically well-defined training sets, where the semantics of each image is the pathology indicated by that image (for example, normal, hemorrhage, stroke or tumor in neuroimages, or normal v. cancer in microscopic images). The goal of our research is to

1. construct creative statistical image features such that the image semantics are captured with high probabilities;
2. select the most discriminative (across different pathology classes) feature subset from all possible potential indexing features computed from a multimedia, multi-dimensional database;
3. use the most discriminative feature-subset as the front-end index to find (for image classification or retrieval) medically similar cases in a large image database to aid diagnosis, surgical planning, patient treatment, outcome evaluation and medical education.

Our approach is a principled method firmly rooted in Bayes decision theory. Techniques in memory-based learning, feature selection and statistical regression are adopted in our system to achieve classification-driven, semantic based image analysis, indexing and retrieval.

Name - Title <Email Address>
• [Home] Takeo Kanade - U.A. and Helen Whitaker University Prof., RI/CS <tk@cs.cmu.edu>
• Nicole Lazar - Assistant Professor, Statistics <nlazar@stat.cmu.edu>
• [Home] Yanxi Liu - Adjunct Associate Research Professor <yanxi@cs.cmu.edu>
• Leonid Teverovskiy - PhD Student, MLD <lteverov@andrew.cmu.edu>

• Quantified Brain Asymmetry for Age Estimation of Normal and Alzheimer's Disease/Mild Cognitive Impairment Subjects
  L. Teverovskiy, J. Becker, O. Lopez, and Y. Liu
  5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, May, 2008.

• Discovery of "Biomarkers" for Alzheimer's Disease Prediction from Structural MR Images
  Y. Liu, L. Teverovskiy, O. Lopez, H. Aizenstein, C. Meltzer, and J. Becker
  2007 IEEE International Symposium on Biomedical Imaging, April, 2007.

• Feature-based vs. Intensity-based Neuroimage Registration: Comprehensive Comparison Using Mutual Information
  L. Teverovskiy, O. Carmichael, H. Aizenstein, N. Lazar, and Y. Liu
  2007 IEEE International Symposium on Biomedical Imaging, April, 2007.

• Learning-based Neuroimage Registration
  L. Teverovskiy and Y. Liu
  tech. report CMU-RI-TR-04-59, Robotics Institute, Carnegie Mellon University, October, 2004. [Abstract]
  Download: pdf [321 KB], ps.gz [1798 KB] copyrighted

• Truly 3D Midsagittal Plane Extraction for Robust Neuroimage Registration
  L. Teverovskiy and Y. Liu
  tech. report CMU-RI-TR-04-21, Robotics Institute, Carnegie Mellon University, March, 2004. [Abstract]
  Download: pdf [903 KB], ps.gz [1538 KB] copyrighted

• Semantic based Biomedical Image Indexing and Retrieval
  Y. Liu, N. Lazar, W.E. Rothfus, F. Dellaert, A. Moore, J. Schneider, and T. Kanade
  Trends and Advances in Content-Based Image and Video Retrieval, Shapiro, Kriegel, and Veltkamp, ed., February, 2004. [Abstract]
  Download: pdf [1371 KB] copyrighted

• Semantic-based Biomedical Image Indexing and Retrieval
  Y. Liu, N. Lazar, and W. Rothfus
  International Conference on Diagnostic Imaging and Analysis (ICDIA 2002), August, 2002.
  Download: pdf [539 KB], ps.gz [901 KB] copyrighted

• Measurement of Asymmetry in Persons with Facial Paralysis
  G.S. Wachtman, Y. Liu, T. Zhao, J. Cohn, K. Schmidt, T.C. Henkelmann, J.M. VanSwearingen, and E.K. Manders
  Combined Annual Conference of the Robert H. Ivy and Ohio Valley societies of Plastic and Reconstructive Surgeons, June, 2002.

• Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
  Y. Liu, F. Dellaert, W.E. Rothfus, A. Moore, J. Schneider, and T. Kanade
  Proceedings of the 2001 Medical Imaging Computing and Computer Assisted Intervention Conference (MICCAI '01), Utrecht, The Netherlands, October, 2001.
  Download: pdf [452 KB], ps.gz [652 KB] copyrighted

• Robust Midsagittal Plane Extraction from Normal and Pathological 3D Neuroradiology Images
  Y. Liu, R. Collins, and W.E. Rothfus
  IEEE Transactions on Medical Imaging, Vol. 20, No. 3, March, 2001, pp. 175 - 192. [Abstract]
  Download: pdf [853 KB] copyrighted