Abstract

Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch image resolution and contrast is needed for visualizing sub-cellular complexity of single cells, and (2) the ability to unravel the heterogeneity and complexity of the highly diverse population of cells – a central problem of single-cell analysis in life sciences – is required. We here demonstrate an optofluidic time-stretch imaging flow cytometer that enables these two features, in the context of high-throughput multi-class (up to 14 classes) phytoplantkton screening and classification. Based on the comprehensive feature extraction and selection procedures, we show that the intracellular texture/morphology, which is revealed by high-resolution time-stretch imaging, plays a critical role of improving the accuracy of phytoplankton classification, as high as 94.7%, based on multi-class support vector machine (SVM). We also demonstrate that high-resolution time-stretch images, which allows exploitation of various feature domains, e.g. Fourier space, enables further sub-population identification – paving the way toward deeper learning and classification based on large-scale single-cell images. Not only applicable to biomedical diagnostic, this work is anticipated to find immediate applications in marine and biofuel research.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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2016 (4)

C. Lei, B. Guo, Z. Cheng, and K. Goda, “Optical time-stretch imaging: Principles and applications,” Appl. Phys. Rev. 3(1), 011102 (2016).
[Crossref]

A. K. Lau, H. C. Shum, K. K. Wong, and K. K. Tsia, “Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry,” Lab Chip 16(10), 1743–1756 (2016).
[Crossref] [PubMed]

C. L. Chen, A. Mahjoubfar, L.-C. Tai, I. K. Blaby, A. Huang, K. R. Niazi, and B. Jalali, “Deep Learning in Label-free Cell Classification,” Sci. Rep. 6, 21471 (2016).
[Crossref] [PubMed]

A. K. Lau, A. H. Tang, J. Xu, X. Wei, K. K. Wong, and K. K. Tsia, “Optical Time Stretch for High-Speed and High-Throughput Imaging—From Single-Cell to Tissue-Wide Scales,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–15 (2016).
[Crossref]

2015 (1)

2014 (3)

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

X. Wei, A. K. Lau, T. T. Wong, C. Zhang, K. M. Tsia, and K. K. Wong, “Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm,” IEEE J. Sel. Top. Quantum Electron. 20(5), 384–389 (2014).
[Crossref]

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

2013 (3)

2012 (1)

K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
[Crossref] [PubMed]

2011 (1)

2010 (4)

K. K. Tsia, K. Goda, D. Capewell, and B. Jalali, “Performance of serial time-encoded amplified microscope,” Opt. Express 18(10), 10016–10028 (2010).
[Crossref] [PubMed]

S. Escalera, O. Pujol, and P. Radeva, “On the decoding process in ternary error-correcting output codes,” IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 120–134 (2010).
[Crossref] [PubMed]

Z. V. Finkel, J. Beardall, K. J. Flynn, A. Quigg, T. A. V. Rees, and J. A. Raven, “Phytoplankton in a changing world: cell size and elemental stoichiometry,” J. Plankton Res. 32(1), 119–137 (2010).
[Crossref]

T. M. Mata, A. A. Martins, and N. S. Caetano, “Microalgae for biodiesel production and other applications: a review,” Renew. Sustain. Energy Rev. 14(1), 217–232 (2010).
[Crossref]

2007 (3)

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

H. M. Sosik and R. J. Olson, “Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry,” Limnol. Oceanogr. Methods 5(6), e216 (2007).
[Crossref]

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

2006 (2)

E. J. Buskey and C. J. Hyatt, “Use of the FlowCAM for semi-automated recognition and enumeration of red tide cells (Karenia brevis) in natural plankton samples,” Harmful Algae 5(6), 685–692 (2006).
[Crossref]

K. Rodenacker, B. Hense, U. Jütting, and P. Gais, “Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation,” Microsc. Res. Tech. 69(9), 708–720 (2006).
[Crossref] [PubMed]

2005 (1)

M. Babin, J. C. Cullen, C. S. Roesler, P. L. Donaghay, G. J. Doucette, M. Kahru, M. R. Lewis, C. A. Scholin, M. E. Sieracki, and H. M. Sosik, “New approaches and technologies for observing harmful algal blooms,” Oceanography (Wash. D.C.) 18(2), 210–227 (2005).
[Crossref]

2004 (1)

X. Irigoien, J. Huisman, and R. P. Harris, “Global biodiversity patterns of marine phytoplankton and zooplankton,” Nature 429(6994), 863–867 (2004).
[Crossref] [PubMed]

2002 (1)

C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw. 13(2), 415–425 (2002).
[Crossref] [PubMed]

2001 (1)

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

2000 (1)

E. L. Allwein, R. E. Schapire, and Y. Singer, “Reducing multiclass to binary: A unifying approach for margin classifiers,” J. Mach. Learn. Res. 1, 113–141 (2000).

1998 (1)

M. Bharati and J. MacGregor, “Multivariate image analysis for real-time process monitoring and control,” Ind. Eng. Chem. Res. 37(12), 4715–4724 (1998).
[Crossref]

1993 (2)

T. Chang and C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2(4), 429–441 (1993).
[Crossref] [PubMed]

A. Laine and J. Fan, “Texture classification by wavelet packet signatures,” IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1186–1191 (1993).
[Crossref]

1987 (1)

D. Søballe and B. Kimmel, “A large-scale comparison of factors influencing phytoplankton abundance in rivers, lakes, and impoundments,” Ecology 68(6), 1943–1954 (1987).
[Crossref]

1973 (1)

R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 6(6), 610–621 (1973).
[Crossref]

1962 (1)

M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Inf. Theory 8(2), 179–187 (1962).
[Crossref]

1958 (1)

J. Lund, C. Kipling, and E. Le Cren, “The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting,” Hydrobiologia 11(2), 143–170 (1958).
[Crossref]

Adam, J.

K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
[Crossref] [PubMed]

Allwein, E. L.

E. L. Allwein, R. E. Schapire, and Y. Singer, “Reducing multiclass to binary: A unifying approach for margin classifiers,” J. Mach. Learn. Res. 1, 113–141 (2000).

Ayazi, A.

K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
[Crossref] [PubMed]

Babin, M.

M. Babin, J. C. Cullen, C. S. Roesler, P. L. Donaghay, G. J. Doucette, M. Kahru, M. R. Lewis, C. A. Scholin, M. E. Sieracki, and H. M. Sosik, “New approaches and technologies for observing harmful algal blooms,” Oceanography (Wash. D.C.) 18(2), 210–227 (2005).
[Crossref]

Beardall, J.

Z. V. Finkel, J. Beardall, K. J. Flynn, A. Quigg, T. A. V. Rees, and J. A. Raven, “Phytoplankton in a changing world: cell size and elemental stoichiometry,” J. Plankton Res. 32(1), 119–137 (2010).
[Crossref]

Benfield, M.

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

Bharati, M.

M. Bharati and J. MacGregor, “Multivariate image analysis for real-time process monitoring and control,” Ind. Eng. Chem. Res. 37(12), 4715–4724 (1998).
[Crossref]

Blaby, I. K.

C. L. Chen, A. Mahjoubfar, L.-C. Tai, I. K. Blaby, A. Huang, K. R. Niazi, and B. Jalali, “Deep Learning in Label-free Cell Classification,” Sci. Rep. 6, 21471 (2016).
[Crossref] [PubMed]

Brackbill, N.

K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
[Crossref] [PubMed]

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn. 45(1), 5–32 (2001).
[Crossref]

Buskey, E. J.

E. J. Buskey and C. J. Hyatt, “Use of the FlowCAM for semi-automated recognition and enumeration of red tide cells (Karenia brevis) in natural plankton samples,” Harmful Algae 5(6), 685–692 (2006).
[Crossref]

Caetano, N. S.

T. M. Mata, A. A. Martins, and N. S. Caetano, “Microalgae for biodiesel production and other applications: a review,” Renew. Sustain. Energy Rev. 14(1), 217–232 (2010).
[Crossref]

Capewell, D.

Carlo, D. D.

Chan, A. C.

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

Chan, G. C.

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

Chan, R. K.

Chang, T.

T. Chang and C. J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Trans. Image Process. 2(4), 429–441 (1993).
[Crossref] [PubMed]

Chen, C.

Chen, C. L.

C. L. Chen, A. Mahjoubfar, L.-C. Tai, I. K. Blaby, A. Huang, K. R. Niazi, and B. Jalali, “Deep Learning in Label-free Cell Classification,” Sci. Rep. 6, 21471 (2016).
[Crossref] [PubMed]

Cheng, Z.

C. Lei, B. Guo, Z. Cheng, and K. Goda, “Optical time-stretch imaging: Principles and applications,” Appl. Phys. Rev. 3(1), 011102 (2016).
[Crossref]

Cullen, J. C.

M. Babin, J. C. Cullen, C. S. Roesler, P. L. Donaghay, G. J. Doucette, M. Kahru, M. R. Lewis, C. A. Scholin, M. E. Sieracki, and H. M. Sosik, “New approaches and technologies for observing harmful algal blooms,” Oceanography (Wash. D.C.) 18(2), 210–227 (2005).
[Crossref]

Culverhouse, P.

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

Dam, H.

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

Davis, C.

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

Di Carlo, D.

K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
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K. Rodenacker, B. Hense, U. Jütting, and P. Gais, “Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation,” Microsc. Res. Tech. 69(9), 708–720 (2006).
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K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
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C. L. Chen, A. Mahjoubfar, L.-C. Tai, I. K. Blaby, A. Huang, K. R. Niazi, and B. Jalali, “Deep Learning in Label-free Cell Classification,” Sci. Rep. 6, 21471 (2016).
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Radeva, P.

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M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
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Shum, H. C.

A. K. Lau, H. C. Shum, K. K. Wong, and K. K. Tsia, “Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry,” Lab Chip 16(10), 1743–1756 (2016).
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T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

Sieracki, M.

M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki, A. Lopez-Urrutia, H. Dam, Q. Hu, C. Davis, A. Hanson, C. Pilskaln, E. Riseman, H. Schulz, P. Utgoff, and G. Gorsky, “RAPID: research on automated plankton identification,” Oceanography (Wash. D.C.) 20(2), 172–187 (2007).
[Crossref]

Sieracki, M. E.

M. Babin, J. C. Cullen, C. S. Roesler, P. L. Donaghay, G. J. Doucette, M. Kahru, M. R. Lewis, C. A. Scholin, M. E. Sieracki, and H. M. Sosik, “New approaches and technologies for observing harmful algal blooms,” Oceanography (Wash. D.C.) 18(2), 210–227 (2005).
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[Crossref]

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K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan, E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray, C. Wang, N. Brackbill, D. Di Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” Proc. Natl. Acad. Sci. U.S.A. 109(29), 11630–11635 (2012).
[Crossref] [PubMed]

Sosik, H. M.

H. M. Sosik and R. J. Olson, “Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry,” Limnol. Oceanogr. Methods 5(6), e216 (2007).
[Crossref]

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

M. Babin, J. C. Cullen, C. S. Roesler, P. L. Donaghay, G. J. Doucette, M. Kahru, M. R. Lewis, C. A. Scholin, M. E. Sieracki, and H. M. Sosik, “New approaches and technologies for observing harmful algal blooms,” Oceanography (Wash. D.C.) 18(2), 210–227 (2005).
[Crossref]

Tai, L.-C.

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[Crossref] [PubMed]

Tang, A. H.

A. K. Lau, A. H. Tang, J. Xu, X. Wei, K. K. Wong, and K. K. Tsia, “Optical Time Stretch for High-Speed and High-Throughput Imaging—From Single-Cell to Tissue-Wide Scales,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–15 (2016).
[Crossref]

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

Tang, M. T.

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
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T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
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A. K. Lau, H. C. Shum, K. K. Wong, and K. K. Tsia, “Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry,” Lab Chip 16(10), 1743–1756 (2016).
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[Crossref]

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[Crossref]

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[Crossref]

X. Wei, A. K. Lau, T. T. Wong, C. Zhang, K. M. Tsia, and K. K. Wong, “Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm,” IEEE J. Sel. Top. Quantum Electron. 20(5), 384–389 (2014).
[Crossref]

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

Wong, K. K.

A. K. Lau, H. C. Shum, K. K. Wong, and K. K. Tsia, “Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry,” Lab Chip 16(10), 1743–1756 (2016).
[Crossref] [PubMed]

A. K. Lau, A. H. Tang, J. Xu, X. Wei, K. K. Wong, and K. K. Tsia, “Optical Time Stretch for High-Speed and High-Throughput Imaging—From Single-Cell to Tissue-Wide Scales,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–15 (2016).
[Crossref]

X. Wei, A. K. Lau, T. T. Wong, C. Zhang, K. M. Tsia, and K. K. Wong, “Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm,” IEEE J. Sel. Top. Quantum Electron. 20(5), 384–389 (2014).
[Crossref]

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

Wong, T. T.

T. T. Wong, A. K. Lau, K. K. Ho, M. Y. Tang, J. D. Robles, X. Wei, A. C. Chan, A. H. Tang, E. Y. Lam, K. K. Wong, G. C. Chan, H. C. Shum, and K. K. Tsia, “Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow,” Sci. Rep. 4, 3656 (2014).
[Crossref] [PubMed]

A. K. Lau, T. T. Wong, K. K. Ho, M. T. Tang, A. C. Chan, X. Wei, E. Y. Lam, H. C. Shum, K. K. Wong, and K. K. Tsia, “Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm,” J. Biomed. Opt. 19(7), 076001 (2014).
[Crossref] [PubMed]

X. Wei, A. K. Lau, T. T. Wong, C. Zhang, K. M. Tsia, and K. K. Wong, “Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm,” IEEE J. Sel. Top. Quantum Electron. 20(5), 384–389 (2014).
[Crossref]

Wu, J.

Xu, J.

A. K. Lau, A. H. Tang, J. Xu, X. Wei, K. K. Wong, and K. K. Tsia, “Optical Time Stretch for High-Speed and High-Throughput Imaging—From Single-Cell to Tissue-Wide Scales,” IEEE J. Sel. Top. Quantum Electron. 22(4), 1–15 (2016).
[Crossref]

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X. Wei, A. K. Lau, T. T. Wong, C. Zhang, K. M. Tsia, and K. K. Wong, “Coherent laser source for high frame-rate optical time-stretch microscopy at 1.0 μm,” IEEE J. Sel. Top. Quantum Electron. 20(5), 384–389 (2014).
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Figures (6)

Fig. 1
Fig. 1 Schematic of an optofluidic time-stretch imaging system for imaging flow cytometry of phytoplankton. BPL: broadband pulsed laser; SMF: 10 km single mode fiber operating at 1060nm; YDFA: homemade Yitterbium-doped fiber amplifier; FC: fiber collimator; BP: beamsplitter; DG: diffraction grating of 1200 grooves/mm; OBJ1: 0.75NA objective lens; OBJ2: 0.8NA objective lens; M1, M2: mirrors; PD: 12GHz single-pixel photodetector; OSC: oscilloscope with a bandwidth of 16GHz and a sampling rate of 80GSa/s. The top inset shows the pulse shapes in the wavelength and time domains at different stages (1, 2, and 3), from wavelength-time mapping to space-wavelength mapping.
Fig. 2
Fig. 2 (a)Selected time-stretch images of 14 species of microalgae in the genus level (flow rate = 2m/s, line-scan rate = 11 MHz) . The scale bar is in 20 µm for all images. A: Synura; B: Thalassiosira; C: Scenedesmus; D: Selenastrum capricornutum; E: Chorella; F: Gymnodium; G: Gymnodinium; H: Prorocentrum; I: Euglena; J: Lyngbya; K: Spirulina Major; L: Merismopedia; M: Chaetoceros gracilis; N: Navicula. (b) Cropped and magnified section (dashed red and green box) showing the sub-cellular structures of the Gymnodium (F) and Euglena (I), and a cross-sectional profile of Spirulina Major (K).
Fig. 3
Fig. 3 (a) Flow chart of the image processing and classification pipeline. (b) Selected images of the intermediate stage including (A) the original gray-scale image, (B) the local entropy map, (C) the blob image and (D) the binary mask.
Fig. 4
Fig. 4 (a) A bar plot of feature importance evaluated by the bagging (or bootstrap aggregation) approach based on the random forest algorithm. For each feature (represented by a bar with an assigned number (1-44) as explained in Table 3 in Appendix), the feature importance value is the increase in mean squared error (MSE) averaged over all 500 classification trees in the forest, which is divided by the standard deviation taken over the trees, for each feature [27,28]. The low resolution features are highlighted as yellow bars in the plot – most of which show low importance for classification. (b) 10-fold cross-validated accuracy of the multi-class SVM against the numbers of the selected features according to the descending ranking order, i.e. starting from the highest importance feature to the lowest one.
Fig. 5
Fig. 5 (a) The confusion matrix, and (b) the accuracy of each species of the classification result of time-stretch captured phytoplankton images based on the optimized SVM model. (c) Comparison of the accuracy of the multi-class SVM classification by using low-resolution (i.e. geometrics and shape features as highlighted in yellow in Fig. 4(a)) and the selected 26 features. The labels refer to 1:Selenastrum capricornutum; 2:Chlamydomonas; 3:Scenedesmus; 4:Chaetoceros gracilis; 5:Gymnodinium; 6:Navicula; 7:Prorocentrum; 8:Thalassiosira; 9: Merismopedia; 10: Spirulina major; 11:Chlorella; 12: Euglena; 13: Synura; 14: Lyngbya.
Fig. 6
Fig. 6 Scatter plot for sub-type classification of the dense (blue) and hollow (red) types of Scenedesmus based on the averaged power values of the three annular-ring bands in the 2D Fourier transformation (FT) of the images (highlighted as yellow circles in the power spectra; also indicated as 1,2,3 at the left top corner of the scatter plot). Selected images and the corresponding 2D Fourier transformation for the dense type and the hollow gelatinous type of Scenedesmus are also shown.

Tables (3)

Tables Icon

Table 1 Summary of the Extracted and Selected Features, as well as the Corresponding Image Processing Steps Involved for Feature Extraction

Tables Icon

Table 2 Assigned Code Words in the One-Vs-One Strategy

Tables Icon

Table 3 Summary of Features Extracted from each Time-stretch Image

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

κ(x,y)= e γ||xy| | 2 ,
pdf=P(g)=h(g)/M
M CM = x=1 N y=1 N { 0, otherwise 1if I(x,y)=i and I(x+ d y ,y+ d y )=j
μ x = i=0 L1 i j=0 L1 M CM (i,j) and μ y = i=0 L1 j=0 L1 j M CM (i,j)
σ x = i=0 L1 (i μ x ) 2 j=0 L1 M CM (i,j) and σ y = j=0 L1 (i μ y ) 2 i=0 L1 M CM (i,j)

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