Learning Multispectral Texture Features for Cervical Cancer Detection

Yanxi Liu, Tong Zhao, and Jiayong Zhang
Proceedings of 2002 IEEE International Symposium on BiomedicalImaging: Macro to Nano, July, 2002, pp. 169 - 172.


Download
  • Adobe portable document format (pdf) (555KB)
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
We present a bottom-up approach for automatic cancer cell detection in multispectral microscopic thin Pap smear images. Around 4,000 multispectral texture features are explored for cancer cell detection. Using two feature screening measures, the initial feature set is effectively reduced to a computationally manageable size. Based on pixel-level screening results, cancerous regions can thus be detected through a relatively simple procedure. Our experiments have demonstrated the potential of both multispectral and texture information to serve as valuable complementary cues to traditional detection methods.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Medical Robotics and Computer Assisted Surgery and Biomedical Image Analysis
Associated Project(s): Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer
Number of pages: 4

Text Reference
Yanxi Liu, Tong Zhao, and Jiayong Zhang, "Learning Multispectral Texture Features for Cervical Cancer Detection," Proceedings of 2002 IEEE International Symposium on BiomedicalImaging: Macro to Nano, July, 2002, pp. 169 - 172.

BibTeX Reference
@inproceedings{Liu_2002_3995,
   author = "Yanxi Liu and Tong Zhao and Jiayong Zhang",
   title = "Learning Multispectral Texture Features for Cervical Cancer Detection",
   booktitle = "Proceedings of 2002 IEEE International Symposium on BiomedicalImaging: Macro to Nano",
   pages = "169 - 172",
   month = "July",
   year = "2002",
}