Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine

Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine Publisher

Bakhtiaridoost S¹ ; Habibiyan H¹ ; Muhammadnejad S² ; Haddadi M³ ; Ghafoorifard H⁴ ; Arabalibeik H⁵ ; Amanpour S³

Show Affiliations

1. Photonics Engineering Group, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
2. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
3. Cancer Models Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
4. Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
5. Research Center for Biomedical Technology and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran

Source: RSC Advances Published:2016

Abstract

Wavelet transform is a powerful mathematical tool for signal processing. Its high capabilities including signal decomposition, compressing and de-noising make it useful for numerous applications, especially for processing weak signals such as in Raman spectra. Raman spectroscopy is a highly accurate and non-destructive method to identify materials, so it can be an appropriate mechanism for the analysis of circulating tumor cells (CTCs). In this paper we present a method comprising of Raman spectroscopy and wavelet transform to distinguish between different types of leukocytes and CTCs derived from breast cancer (MCF7). All types of leukocytes are isolated from the peripheral blood of healthy donors while CTCs are provided from cell cultures. All of the cells are dried on quartz discs. Raman spectra are collected from cells that are excited with a 785 nm laser. Wavelet transform is used for signal pre-processing such as background correction and signal de-noising. Then a discrete wavelet transform is used as a feature extraction tool. At the end of the process, support vector machine (SVM) is used to classify the spectra into two groups including leukocytes and CTCs. Distinguishing between the cells is achieved with an accuracy of more than 98.99%. © 2016 The Royal Society of Chemistry.

Style	Citing Format
MLA	Bakhtiaridoost S, et al.. "Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine." RSC Advances, vol. 6, no. 55, 2016, pp. 50027-50033.
APA	Bakhtiaridoost S, Habibiyan H, Muhammadnejad S, Haddadi M, Ghafoorifard H, Arabalibeik H, Amanpour S (2016). Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine. RSC Advances, 6(55), 50027-50033.
Chicago	Bakhtiaridoost S, Habibiyan H, Muhammadnejad S, Haddadi M, Ghafoorifard H, Arabalibeik H, Amanpour S. "Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine." RSC Advances 6, no. 55 (2016): 50027-50033.
Harvard	Bakhtiaridoost S et al. (2016) 'Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine', RSC Advances, 6(55), pp. 50027-50033.
Vancouver	Bakhtiaridoost S, Habibiyan H, Muhammadnejad S, Haddadi M, Ghafoorifard H, Arabalibeik H, et al.. Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine. RSC Advances. 2016;6(55):50027-50033.
BibTex	@article{ author = {Bakhtiaridoost S and Habibiyan H and Muhammadnejad S and Haddadi M and Ghafoorifard H and Arabalibeik H and Amanpour S}, title = {Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine}, journal = {RSC Advances}, volume = {6}, number = {55}, pages = {50027-50033}, year = {2016} }
RIS	TY - JOUR AU - Bakhtiaridoost S AU - Habibiyan H AU - Muhammadnejad S AU - Haddadi M AU - Ghafoorifard H AU - Arabalibeik H AU - Amanpour S TI - Raman Spectroscopy-Based Label-Free Cell Identification Using Wavelet Transform and Support Vector Machine JO - RSC Advances VL - 6 IS - 55 SP - 50027 EP - 50033 PY - 2016 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract