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Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer
This project is no longer active.

Head: Yanxi Liu
Contact: Yanxi Liu (yanxi@cs.cmu.edu)

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

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

We are exploring cutting edge optical imaging technology combined with robotics and computer vision technology and statistical learning algorithms to find the most discriminative image features for cancer detection and diagnosis. This work is carried out by an interdisciplinary team of researchers from both Carnegie Mellon University and University of Pittsburgh, and is supported by the unconventional innovation program of National Cancer Institute.

The research focus in the project is twofold:

1. develop methodology for image feature extraction from multispectral biological images, find the most discriminating feature subsets, achieve high classification rates with minimum false positive rate, and provide direct feedback to the imaging process.
2. construct a 3D robotics imaging system using multiple cameras and lighting sources for detection of skin cancer through reconstruction of 4D spatiotemporal images.

Name - Title <Email Address>
• [Home] Goksel Dedeoglu - PhD Student, RI
• Daniel Farkas - Adjunct Faculty (Adjunct)
• [Home] Takeo Kanade - U.A. and Helen Whitaker University Prof., RI/CS <tk@cs.cmu.edu>
• [Home] Yanxi Liu - Adjunct Associate Research Professor <yanxi@cs.cmu.edu>
• Elliot S. Wachman - Special Faculty, Center for Light Microscope Imaging and Biotechnology <ew2w@andrew.cmu.edu>
• [Home] Jiayong Zhang - PhD Student, RI
• Tong Zhao - Principal Research Engineer

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• SVM Decision Boundary Based Discriminative Subspace Induction
  J. Zhang and Y. Liu
  Pattern Recognition, Vol. 38, No. 10, October, 2005, pp. 1746 - 1758. [Abstract]
  Download: pdf [645 KB] copyrighted

• Cervical Cancer Detection Using SVM Based Feature Screening
  J. Zhang and Y. Liu
  Proceedings of the 7th International Conference on Medical Image Computing and Computer Aided Intervention (MICCAI '04), Vol. II, September, 2004, pp. 873 - 880.
  Download: pdf [850 KB] copyrighted

• Does Multispectral Texture Features Really Improve Cervical Cancer Detection?
  T. Zhao, J. Zhang, and Y. Liu
  International Conference on Diagnostic Imaging and Analysis (ICDIA 2002), August, 2002.
  Download: pdf [127 KB] copyrighted

• SVM Based Feature Screening Applied to Hierarchical Cervial Cancer Detection
  J. Zhang, Y. Liu, and T. Zhao
  International Conference on Diagnostic Imaging and Analysis (ICDIA 2002), August, 2002. [Abstract]
  Download: pdf [488 KB] copyrighted

• Learning Multispectral Texture Features for Cervical Cancer Detection
  Y. Liu, T. Zhao, and J. Zhang
  Proceedings of 2002 IEEE International Symposium on BiomedicalImaging: Macro to Nano, July, 2002, pp. 169 - 172. [Abstract]
  Download: pdf [554 KB], ps.gz [876 KB] copyrighted

• Learning Discriminant Features in Multispectral Biological images
  Y. Liu
  tech. report CMU-RI-TR-01-24, Robotics Institute, Carnegie Mellon University, 2001.