Abstract
Liver cancer usually has a high degree of malignancy and its early
symptoms are hidden, therefore, it is of significant research value to
develop early-stage detection methods of liver cancer for pathological
screening. In this paper, a biometric detection method for living
human hepatocytes based on terahertz time-domain spectroscopy was
proposed. The difference in terahertz response between normal and
cancer cells was analyzed, including five characteristic parameters in
the response, namely refractive index, absorption coefficient,
dielectric constant, dielectric loss and dielectric loss tangent.
Based on class separability and variable correlation, absorption
coefficient and dielectric loss were selected to better characterize
cellular properties. Maximum information coefficient and principal
component analysis were employed for feature extraction, and a cell
classification model of support vector machine was constructed. The
results showed that the algorithm based on parameter feature fusion
can achieve an accuracy of 91.6% for human hepatoma cell lines
and one normal cell line. This work provides a promising solution for
the qualitative evaluation of living cells in liquid environment.
Funder
Key Technology Research and Development
Program of Zhejiang Province
State Key Laboratory of Industrial
Control Technology Program of Zhejiang University
National Natural Science Foundation of
China
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
1 articles.
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