RI | Research | Projects | Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer

Text only version of this site

Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer This project is no longer active.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

Jump to: Project Description | Personnel | Publications

Project Description

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.

Past members

	Name	Title	Email Address
	Goksel Dedeoglu	PhD Student, RI
	Daniel Farkas	Adjunct Faculty (Adjunct)
	Takeo Kanade	U.A. and Helen Whitaker University Prof., RI/CS	tk@cs.cmu.edu
	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
	Jiayong Zhang	PhD Student, RI
	Tong Zhao	Principal Research Engineer

Publications

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.