| DOI | Resolve DOI: https://doi.org/10.1007/978-3-031-10467-1_12 |
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| Author | Search for: Mozaffari, M. Hamed1; Search for: Tay, Li-Lin1 |
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| Affiliation | - National Research Council Canada. Metrology Research Centre
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| Format | Text, Article |
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| Conference | Computing Conference 2022, July 14-15, 2022, London, United Kingdom |
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| Subject | Raman spectroscopy; independent component analysis; machine learning; hyperspectral unmixing; single human cell analysis; blind source separation |
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| Abstract | Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman map is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a raster pattern on a 0.5-µm grid. Unlike previously used unsupervised machine learning techniques such as principal component analysis (PCA), ICA is based on non-Gaussianity and statistical independence of data which is the case for mixture Raman spectra. Hence, ICA is a great candidate for assembling pseudo-colour maps from the spectral hypercube of Raman spectra. Our experimental results revealed that ICA is capable of reconstructing false colour maps of Raman hyperspectral data of human cells, showing the nuclear region constituents as well as subcellular organelle in the cytoplasm and distribution of mitochondria in the perinuclear region. Minimum pre-processing requirements and label-free nature of the ICA method make it a great unmixed method for extraction of endmembers in Raman hyperspectral maps of living cells. |
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| Publication date | 2022-07-07 |
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| Publisher | Springer Nature Switzerland AG |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4814e373-b498-4bc5-b9bc-38228b9559d1 |
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| Record created | 2023-07-07 |
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| Record modified | 2023-07-07 |
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