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Carnegie Mellon Robotics Institute

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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",
}

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