Abstract

In Raman spectroscopy, it is crucial to focus the laser on the sample in order to guarantee the intensity and repeatability of the characteristic peaks, which is known as autofocus. In this paper, we propose a novel low-cost scheme based on the subtle placement of the laser source and the image sensor. We confirm the feasibility of monitoring the focus status through the centroid position of the laser spot’s image (CPSI) in theory. Both the simulation and experimental results illustrate that the distance-ordinate function is similar in shape to the logarithm, which not only helps to shorten the autofocus time but also achieves the sub-decimeter measuring range and micrometer resolution near the focal point. Meanwhile, we discuss in detail how to obtain the desired performance by adjusting the extrinsic camera parameters and the way to overcome the disturbance of the noise, ambient light and non-normal incidence. An autofocus-free handheld Raman spectrograph utilizes this method to autofocus the alcohol in the centrifuge tube successfully and the spectral reproducibility is improved. Our results may pave the way to a novel autofocus approach for Raman mapping in vivo.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
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    [Crossref]
  34. K. Suzuki and S. Kubota, “Understanding the exposure-time effect on speckle contrast measurement for laser projection with rotating diffuser,” Opt. Rev. 26, 145–151 (2019).
    [Crossref]
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    [Crossref]

2019 (7)

A. Culka and J. Jehlička, “A database of raman spectra of precious gemstones and minerals used as cut gems obtained using portable sequentially shifted excitation raman spectrometer,” J. Raman Spectrosc. 50, 262–280 (2019).
[Crossref]

Z. Huang, A. Zhang, and D. Cui, “Nanomaterial-based sers sensing technology for biomedical application,” J. Mater. Chem. B 7, 3755–3774 (2019).
[Crossref]

G. Maculotti, X. Feng, R. Su, M. Galetto, and R. Leach, “Residual flatness and scale calibration for a point autofocus surface topography measuring instrument,” Meas. Sci. Technol. 30, 075005 (2019).
[Crossref]

K. Suzuki and S. Kubota, “Understanding the exposure-time effect on speckle contrast measurement for laser projection with rotating diffuser,” Opt. Rev. 26, 145–151 (2019).
[Crossref]

P. Samyn, D. Vandamme, P. Adriaensens, and R. Carleer, “Surface chemistry of oil-filled organic nanoparticle coated papers analyzed using micro-raman mapping,” Appl. Spectrosc. 73, 67–77 (2019).

Y. Wang, Y. Wang, L. Liu, and X. Chen, “Defocused camera calibration with a conventional periodic target based on fourier transform,” Opt. Lett. 44, 3254–3257 (2019).
[Crossref] [PubMed]

G. Saerens, L. Lang, C. Renaut, F. Timpu, V. Vogler-Neuling, C. Durand, M. Tchernycheva, I. Shtrom, A. Bouravleuv, R. Grange, and M. Timofeeva, “Image-based autofocusing system for nonlinear optical microscopy with broad spectral tuning,” Opt. Express 27, 19915–19930 (2019).
[Crossref]

2018 (4)

Z. Zhou, C. Li, T. He, C. Lan, P. Sun, Y. Zheng, Y. Yin, and Y. Liu, “Facile large-area autofocusing raman mapping system for 2d material characterization,” Opt. Express 26, 9071–9080 (2018).
[Crossref] [PubMed]

C. J. Corden, D. W. Shipp, P. Matousek, and I. Notingher, “Fast raman spectral mapping of highly fluorescing samples by time-gated spectral multiplexed detection,” Opt. Lett. 43, 5733–5736 (2018).
[Crossref] [PubMed]

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
[Crossref] [PubMed]

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

2017 (4)

Y. Jin, A. P. Kotula, C. R. Snyder, A. R. Hight Walker, K. B. Migler, and Y. J. Lee, “Raman identification of multiple melting peaks of polyethylene,” Macromolecules 50, 6174–6183 (2017).
[Crossref]

S. Polisetti, N. F. Baig, N. Morales-Soto, J. D. Shrout, and P. W. Bohn, “Spatial mapping of pyocyanin in pseudomonas aeruginosa bacterial communities using surface enhanced raman scattering,” Appl. Spectrosc. 71, 215–223 (2017).
[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).
[Crossref]

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for raman spectrometry and its application,” J. Opt. 46, 62–67 (2017).
[Crossref]

2016 (2)

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

2014 (5)

H. Yoon and D. H. Lee, “New approaches to gastric cancer staging: Beyond endoscopic ultrasound, computed tomography and positron emission tomography,” World J. Gastroenterol. 20, 13783–13790 (2014).
[Crossref] [PubMed]

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

C. Liu and K. Lin, “Numerical and experimental characterization of reducing geometrical fluctuations of laser beam based on rotating optical diffuser,” Opt. Eng. 53, 122408 (2014).
[Crossref]

S. Li, G. Chen, Y. Zhang, Z. Guo, Z. Liu, J. Xu, X. Li, and L. Lin, “Identification and characterization of colorectal cancer using raman spectroscopy and feature selection techniques,” Opt. Express 22, 25895–25908 (2014).
[Crossref] [PubMed]

X. Zhang, Z. Liu, M. Jiang, and M. Chang, “Fast and accurate auto-focusing algorithm based on the combination of depth from focus and improved depth from defocus,” Opt. Express 22, 31237–31247 (2014).
[Crossref]

2013 (1)

C. Liu, Y. Lin, and P. Hu, “Design and characterization of precise laser-based autofocusing microscope with reduced geometrical fluctuations,” Microsyst. Technol. 19, 1717–1724 (2013).
[Crossref]

2012 (1)

C. Liu, P. Hu, and Y. Lin, “Design and experimental validation of novel optics-based autofocusing microscope,” Appl. Phys. B 109, 259–268 (2012).
[Crossref]

2011 (1)

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

2009 (2)

H. G. Rhee, D. I. Kim, and Y. W. Lee, “Realization and performance evaluation of high speed autofocusing for direct laser lithography,” Rev. Sci. Instruments 80, 073103 (2009).
[Crossref]

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

2006 (1)

J. Kannala and S. S. Brandt, “A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1335–1340 (2006).
[Crossref]

2004 (1)

T. Nakayoshi, H. Tajiri, K. Matsuda, M. Kaise, M. Ikegami, and H. Sasaki, “Magnifying endoscopy combined with narrow band imaging system for early gastric cancer: Correlation of vascular pattern with histopathology (including video),” Endoscopy 36, 1080–1084 (2004).
[Crossref] [PubMed]

2003 (1)

J. He, R. Zhou, and Z. Hong, “Modified fast climbing search auto-focus algorithm with adaptive step size searching technique for digital camera,” IEEE Trans. Consumer Electron. 49, 257–262 (2003).
[Crossref]

2002 (1)

N. Stone, C. Kendall, N. Shepherd, P. Crow, and H. Barr, “Near-infrared raman spectroscopy for the classification of epithelial pre-cancers and cancers,” J. Raman Spectrosc. 33, 564–573 (2002).
[Crossref]

2000 (1)

X. Tang, P. L’Hostis, and Y. Xiao, “An auto-focusing method in a microscopic testbed for optical discs,” J. Res. Natl. Inst. Standards Technol. 105, 565–569 (2000).
[Crossref]

1997 (1)

K. H. Ong, J. C. H. Phang, and J. T. L. Thong, “A robust focusing and astigmatism correction method for the scanning electron microscope,” Scanning 19, 553–563 (1997).
[Crossref]

1992 (1)

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Transactions on Pattern Analysis Mach. Intell. 14, 965–980 (1992).
[Crossref]

1990 (1)

Adriaensens, P.

Alfranca, G.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Ashton, L.

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

Aslam, M. A.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Baig, N. F.

Barr, H.

N. Stone, C. Kendall, N. Shepherd, P. Crow, and H. Barr, “Near-infrared raman spectroscopy for the classification of epithelial pre-cancers and cancers,” J. Raman Spectrosc. 33, 564–573 (2002).
[Crossref]

Bird, B.

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

Bohn, P. W.

Bouravleuv, A.

Brandt, S. S.

J. Kannala and S. S. Brandt, “A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1335–1340 (2006).
[Crossref]

Butler, H. J.

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

Carleer, R.

Chang, J.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Chang, M.

Chen, D.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Chen, F.

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

Chen, G.

Chen, J.

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

Chen, N.

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

Chen, P.

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

Chen, R.

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

Chen, X.

Chen, Y.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Cheng, S.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Chu, B.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Cinque, G.

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

Cohen, P.

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Shrout, J. D.

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Y. Jin, A. P. Kotula, C. R. Snyder, A. R. Hight Walker, K. B. Migler, and Y. J. Lee, “Raman identification of multiple melting peaks of polyethylene,” Macromolecules 50, 6174–6183 (2017).
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T. Nakayoshi, H. Tajiri, K. Matsuda, M. Kaise, M. Ikegami, and H. Sasaki, “Magnifying endoscopy combined with narrow band imaging system for early gastric cancer: Correlation of vascular pattern with histopathology (including video),” Endoscopy 36, 1080–1084 (2004).
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Tang, J.

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

Tang, X.

X. Tang, P. L’Hostis, and Y. Xiao, “An auto-focusing method in a microscopic testbed for optical discs,” J. Res. Natl. Inst. Standards Technol. 105, 565–569 (2000).
[Crossref]

Tchernycheva, M.

Thong, J. T. L.

K. H. Ong, J. C. H. Phang, and J. T. L. Thong, “A robust focusing and astigmatism correction method for the scanning electron microscope,” Scanning 19, 553–563 (1997).
[Crossref]

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Timpu, F.

Vandamme, D.

Vogler-Neuling, V.

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[Crossref] [PubMed]

Wang, C.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Wang, K.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Wang, P.

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
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Wang, Y.

Y. Wang, Y. Wang, L. Liu, and X. Chen, “Defocused camera calibration with a conventional periodic target based on fourier transform,” Opt. Lett. 44, 3254–3257 (2019).
[Crossref] [PubMed]

Y. Wang, Y. Wang, L. Liu, and X. Chen, “Defocused camera calibration with a conventional periodic target based on fourier transform,” Opt. Lett. 44, 3254–3257 (2019).
[Crossref] [PubMed]

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for raman spectrometry and its application,” J. Opt. 46, 62–67 (2017).
[Crossref]

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

Weng, J.

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Transactions on Pattern Analysis Mach. Intell. 14, 965–980 (1992).
[Crossref]

Wu, Y.

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

Xiao, Y.

X. Tang, P. L’Hostis, and Y. Xiao, “An auto-focusing method in a microscopic testbed for optical discs,” J. Res. Natl. Inst. Standards Technol. 105, 565–569 (2000).
[Crossref]

Xu, J.

Xu, X.

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

Yan, S.

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
[Crossref] [PubMed]

Yin, Y.

Yoon, H.

H. Yoon and D. H. Lee, “New approaches to gastric cancer staging: Beyond endoscopic ultrasound, computed tomography and positron emission tomography,” World J. Gastroenterol. 20, 13783–13790 (2014).
[Crossref] [PubMed]

Yu, Z.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for raman spectrometry and its application,” J. Opt. 46, 62–67 (2017).
[Crossref]

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

Yue, S.

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
[Crossref] [PubMed]

Zeng, H.

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

Zhang, A.

Z. Huang, A. Zhang, and D. Cui, “Nanomaterial-based sers sensing technology for biomedical application,” J. Mater. Chem. B 7, 3755–3774 (2019).
[Crossref]

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Zhang, C.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Zhang, Q.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Zhang, W.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Zhang, X.

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

X. Zhang, Z. Liu, M. Jiang, and M. Chang, “Fast and accurate auto-focusing algorithm based on the combination of depth from focus and improved depth from defocus,” Opt. Express 22, 31237–31247 (2014).
[Crossref]

Zhang, Y.

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

S. Li, G. Chen, Y. Zhang, Z. Guo, Z. Liu, J. Xu, X. Li, and L. Lin, “Identification and characterization of colorectal cancer using raman spectroscopy and feature selection techniques,” Opt. Express 22, 25895–25908 (2014).
[Crossref] [PubMed]

Zhang, Z.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Zhao, B.

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
[Crossref] [PubMed]

Zheng, Y.

Zhi, X.

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Zhou, R.

J. He, R. Zhou, and Z. Hong, “Modified fast climbing search auto-focus algorithm with adaptive step size searching technique for digital camera,” IEEE Trans. Consumer Electron. 49, 257–262 (2003).
[Crossref]

Zhou, Z.

Zou, Y.

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for raman spectrometry and its application,” J. Opt. 46, 62–67 (2017).
[Crossref]

ACS Nano (1)

Y. Chen, Y. Zhang, F. Pan, J. Liu, K. Wang, C. Zhang, S. Cheng, L. Lu, W. Zhang, Z. Zhang, X. Zhi, Q. Zhang, G. Alfranca, J. M. de la Fuente, D. Chen, and D. Cui, “Breath analysis based on surface-enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano 10, 8169–8179 (2016).
[Crossref] [PubMed]

Anal. Chem. (1)

S. Yan, S. Cui, K. Ke, B. Zhao, X. Liu, S. Yue, and P. Wang, “Hyperspectral stimulated raman scattering microscopy unravels aberrant accumulation of saturated fat in human liver cancer,” Anal. Chem. 90, 6362–6366 (2018).
[Crossref] [PubMed]

Appl. Opt. (1)

Appl. Phys. B (1)

C. Liu, P. Hu, and Y. Lin, “Design and experimental validation of novel optics-based autofocusing microscope,” Appl. Phys. B 109, 259–268 (2012).
[Crossref]

Appl. Spectrosc. (2)

Biosens. Bioelectron. (1)

S. Feng, R. Chen, J. Lin, J. Pan, Y. Wu, Y. Li, J. Chen, and H. Zeng, “Gastric cancer detection based on blood plasma surface-enhanced raman spectroscopy excited by polarized laser light,” Biosens. Bioelectron. 26, 3167–3174 (2011).
[Crossref] [PubMed]

Commun. ACM (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).
[Crossref]

Endoscopy (1)

T. Nakayoshi, H. Tajiri, K. Matsuda, M. Kaise, M. Ikegami, and H. Sasaki, “Magnifying endoscopy combined with narrow band imaging system for early gastric cancer: Correlation of vascular pattern with histopathology (including video),” Endoscopy 36, 1080–1084 (2004).
[Crossref] [PubMed]

IEEE Trans. Consumer Electron. (1)

J. He, R. Zhou, and Z. Hong, “Modified fast climbing search auto-focus algorithm with adaptive step size searching technique for digital camera,” IEEE Trans. Consumer Electron. 49, 257–262 (2003).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

J. Kannala and S. S. Brandt, “A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1335–1340 (2006).
[Crossref]

IEEE Transactions on Pattern Analysis Mach. Intell. (1)

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Transactions on Pattern Analysis Mach. Intell. 14, 965–980 (1992).
[Crossref]

J. Biomed. Nanotechnol. (1)

Y. Chen, S. Cheng, A. Zhang, J. Song, J. Chang, K. Wang, G. Gao, Y. Zhang, S. Li, H. Liu, G. Alfranca, M. A. Aslam, B. Chu, C. Wang, F. Pan, L. Ma, J. M. de la Fuente, J. Ni, and D. Cui, “Salivary analysis based on surface enhanced raman scattering sensors distinguishes early and advanced gastric cancer patients from healthy persons,” J. Biomed. Nanotechnol. 14, 1773–1784 (2018).
[Crossref] [PubMed]

J. Mater. Chem. B (1)

Z. Huang, A. Zhang, and D. Cui, “Nanomaterial-based sers sensing technology for biomedical application,” J. Mater. Chem. B 7, 3755–3774 (2019).
[Crossref]

J. Opt. (1)

Z. Sun, B. Song, X. Li, Y. Zou, Y. Wang, Z. Yu, and M. Huang, “A smart optical fiber probe for raman spectrometry and its application,” J. Opt. 46, 62–67 (2017).
[Crossref]

J. Raman Spectrosc. (2)

N. Stone, C. Kendall, N. Shepherd, P. Crow, and H. Barr, “Near-infrared raman spectroscopy for the classification of epithelial pre-cancers and cancers,” J. Raman Spectrosc. 33, 564–573 (2002).
[Crossref]

A. Culka and J. Jehlička, “A database of raman spectra of precious gemstones and minerals used as cut gems obtained using portable sequentially shifted excitation raman spectrometer,” J. Raman Spectrosc. 50, 262–280 (2019).
[Crossref]

J. Res. Natl. Inst. Standards Technol. (1)

X. Tang, P. L’Hostis, and Y. Xiao, “An auto-focusing method in a microscopic testbed for optical discs,” J. Res. Natl. Inst. Standards Technol. 105, 565–569 (2000).
[Crossref]

Macromolecules (1)

Y. Jin, A. P. Kotula, C. R. Snyder, A. R. Hight Walker, K. B. Migler, and Y. J. Lee, “Raman identification of multiple melting peaks of polyethylene,” Macromolecules 50, 6174–6183 (2017).
[Crossref]

Meas. Sci. Technol. (2)

W. Hsu, C. Lee, P. Chen, N. Chen, F. Chen, Z. Yu, C. Kuo, and C. Hwang, “Development of the fast astigmatic auto-focus microscope system,” Meas. Sci. Technol. 20, 045902 (2009).
[Crossref]

G. Maculotti, X. Feng, R. Su, M. Galetto, and R. Leach, “Residual flatness and scale calibration for a point autofocus surface topography measuring instrument,” Meas. Sci. Technol. 30, 075005 (2019).
[Crossref]

Microsyst. Technol. (1)

C. Liu, Y. Lin, and P. Hu, “Design and characterization of precise laser-based autofocusing microscope with reduced geometrical fluctuations,” Microsyst. Technol. 19, 1717–1724 (2013).
[Crossref]

Multimed. Tools Appl. (1)

X. Xu, Y. Wang, X. Zhang, S. Li, X. Liu, and J. Tang, “A comparison of contrast measurements in passive autofocus systems for low contrast images,” Multimed. Tools Appl. 69, 139–156 (2014).
[Crossref]

Nat. Protoc. (1)

H. J. Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J. Fullwood, B. Gardner, P. L. Martin-Hirsch, M. J. Walsh, M. R. McAinsh, N. Stone, and F. L. Martin, “Using raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664–687 (2016).
[Crossref] [PubMed]

Opt. Eng. (1)

C. Liu and K. Lin, “Numerical and experimental characterization of reducing geometrical fluctuations of laser beam based on rotating optical diffuser,” Opt. Eng. 53, 122408 (2014).
[Crossref]

Opt. Express (4)

Opt. Lett. (2)

Opt. Rev. (1)

K. Suzuki and S. Kubota, “Understanding the exposure-time effect on speckle contrast measurement for laser projection with rotating diffuser,” Opt. Rev. 26, 145–151 (2019).
[Crossref]

Rev. Sci. Instruments (1)

H. G. Rhee, D. I. Kim, and Y. W. Lee, “Realization and performance evaluation of high speed autofocusing for direct laser lithography,” Rev. Sci. Instruments 80, 073103 (2009).
[Crossref]

Scanning (1)

K. H. Ong, J. C. H. Phang, and J. T. L. Thong, “A robust focusing and astigmatism correction method for the scanning electron microscope,” Scanning 19, 553–563 (1997).
[Crossref]

World J. Gastroenterol. (1)

H. Yoon and D. H. Lee, “New approaches to gastric cancer staging: Beyond endoscopic ultrasound, computed tomography and positron emission tomography,” World J. Gastroenterol. 20, 13783–13790 (2014).
[Crossref] [PubMed]

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Figures (8)

Fig. 1
Fig. 1 Front and left view of the proposed prototype. The arrows have specified the positive direction of yaw, pitch, and roll angles. v denotes the direction of the spot’s movement.
Fig. 2
Fig. 2 (a) Schematic of the mapping from the laser spot to its image. (b) Δi vs. distance. The simulation parameters are α = 70°, θi = 50 °, θj = 50 °, σx = 35 mm, σz =25 mm, k1 = 0, k2 = 0, ω0 = 0.1 mm, z0 = 50 mm, a = 480, b = 640.
Fig. 3
Fig. 3 Default simulation parameters are α = 70°, θi = 50 °, θj = 50 °, σx = 35 mm, σz = 25 mm, k1 = 0, k2 = 0, ω0 = 0.1 mm, z0 = 50 mm, a = 480, b=640. Δ denotes the difference from the group with the default simulation parameters. (a) Effect of k1 on the centroid’s ordinate. (b) Effect of k1 on the centroid’s abscissa. (c) Effect of k2 on the centroid’s ordinate. Effect of (d) α, (e) σx, and (f) σz on the centroid’s ordinate.
Fig. 4
Fig. 4 (a) Schematic and (b) optical photo of the CPSI autofocus prototype. LF is a premium longpass filter (FELH0800,THORLABS).
Fig. 5
Fig. 5 (a) Initial data and their fitted curve. The effect of the hysteresis on the magnetic grid ruler makes the exact backward data around 100 μm larger than the displayed. Consequently, the actual standard deviation is less than the value in the figure. (b) Centroid’s ordinate vs. ordinal. (c) Scatter diagram and histogram of the centroid at different distances. (d) Quarter of the spot’s grayscale image after reversing. The image is captured in a fixed position at different moments. (e) Centroid’s ordinate after denoising.
Fig. 6
Fig. 6 Centroid of the focused spot collected (a) in dark or illuminated by (b) one Led, (c) two LEDs and (d) three LEDs. (g) Mean of the loss vs. time. (h) Classification of the focus, over-focus and under-focus.
Fig. 7
Fig. 7 Effect of (a) α, (b) σx, (c) σz, (d) k1 and (e) k2. (f) Experimental results of the ordinate-distance curve after calibration. Fitted extrinsic parameters: α = 39.56°, σx = -23.106mm, σz = - 6.97 mm, θi = 30°. Theoretical extrinsic parameters: α = 40°, σx = -25 mm, σz = - 11 mm, θi = 30 °. (g) Diagram of the centroid compensation at the focal point.
Fig. 8
Fig. 8 (a) Flow chart of the centroid-position-based method. (b) Software interface. (c) Spectral difference between alcohol and alcohol + centrifuge tube. (d) Average spectra of the ethyl alcohol and tube in different focus statuses. This experiment isrepeated 10 times to collect data.

Tables (2)

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Table 1 Impact of Extrinsic Parameters on Performance

Tables Icon

Table 2 SM S and SM I with the various focus statuses

Equations (11)

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

( x y z ) = R ( δ , α , γ ) ( x * y * z * ) + T ( σ x , σ y , σ z ) ,
( i j ) = ( a ( z * + x * tan  θ i ) 2 x * tan  θ i b ( y * + x * tan  θ j ) 2 x * tan  θ j ) , z * | x * tan  θ i | , y * | x * tan  θ j | ,
( x y z ) = ( k 2 k 1 1 0 1 0 1 0 0 ) ( r ( x ) sin  ξ r ( x ) cos  ξ x 0 ) , ξ ( 0 , 2 π ) ,
r ( x ) = ω 0 2 ( 1 + ( x f ) 2 z 0 2 ) ,
( x 0 + k 1 y + k 2 z y z ) = ( cos  α 0 sin  α 0 1 0 sin  α 0 cos  α ) ( x * x * tan  θ j ( 2 j b 1 ) x * tan  θ i ( 2 i a 1 ) ) + ( σ x 0 σ z ) ,
σ x x 0 σ z k N , 3 N 4 N 1 tan  θ i cos  α ( 2 i ξ a 1 ) + sin  α = k N , 3 N 4 N 1 cos  α sin  α tan  θ i ( 2 i ξ a 1 ) ,
x 0 = σ z [ tan  α tan  θ i ( 2 i ¯ a ) a ] atan  α + tan  θ i ( 2 i ¯ a ) + σ x ,
x min = {   σ z r tan  ( α + θ i ) + σ x , α < π θ i ,   , α π θ i ,
x max = {   σ z r tan  ( α θ i ) + σ x , α > θ i ,   + , α θ i .
SM S = 1 10 n = 1 10 F n μ m | | F n | | | | μ m | | ,
SM I = 1 N 1 n = 1 10 ( F n μ m ) ( F n μ m ) T ,

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