Abstract

With a perfectly uniform illumination, the amount and concentration of fluorophores in any (biological) sample can be read directly from fluorescence micrographs. However, non-uniform illumination in optical micrographs is a common, yet avoidable artefact, often caused by the setup of the microscope, or by inherent properties caused by the nature of the sample. In this paper, we demonstrate simple matrix-based methods using the common computing environments MATLAB and Python to correct nonuniform illumination, using either a background image or extracting illumination information directly from the sample image, together with subsequent image processing. We compare the processes, algorithms, and results obtained from both MATLAB (commercially available) and Python (freeware). Additionally, we validate our method by evaluating commonly used alternative approaches, demonstrating that the best nonuniform illumination correction can be achieved when a separate background image is available.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Full Article  |  PDF Article
OSA Recommended Articles
Inverse matrix based phase estimation algorithm for structured illumination microscopy

Ruizhi Cao, Youhua Chen, Wenjie Liu, Dazhao Zhu, Cuifang Kuang, Yingke Xu, and Xu Liu
Biomed. Opt. Express 9(10) 5037-5051 (2018)

Adaptive illumination reduces photobleaching in structured illumination microscopy

Nadya Chakrova, Alicia Soler Canton, Christophe Danelon, Sjoerd Stallinga, and Bernd Rieger
Biomed. Opt. Express 7(10) 4263-4274 (2016)

Flat-field illumination for quantitative fluorescence imaging

Ian Khaw, Benjamin Croop, Jialei Tang, Anna Möhl, Ulrike Fuchs, and Kyu Young Han
Opt. Express 26(12) 15276-15288 (2018)

References

  • View by:
  • |
  • |
  • |

  1. A. Köhler, “Ein neues Beleuchtungsverfahren für mikrophotographische Zwecke,” Z. Wiss. Mikrosk. 10, 433–440 (1893).
  2. Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
    [Crossref]
  3. F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
    [Crossref] [PubMed]
  4. D. H. Brainard and B. A. Wandell, “Analysis of the retinex theory of color vision,” J. Opt. Soc. Am. A 3(10), 1651–1661 (1986).
    [Crossref] [PubMed]
  5. D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
    [Crossref] [PubMed]
  6. J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
    [Crossref] [PubMed]
  7. R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
    [Crossref]
  8. J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit. 33(2), 225–236 (2000).
    [Crossref]
  9. J. C. Olivo-Marin, “Extraction of spots in biological images using multiscale products,” Pattern Recognit. 35(9), 1989–1996 (2002).
    [Crossref]
  10. A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
    [Crossref] [PubMed]
  11. P. Nordenfelt, J. M. Cooper, and A. Hochstetter, “Matrix-masking to balance nonuniform illumination in microscopy,” https://nordlab.med.lu.se/?page_id=34 .
  12. T. Ferreira and W. Rasband, ImageJ User Guide, ImageJ/Fij (2012).
  13. The MathWorks Inc, “Correcting Nonuniform Illumination,” https://se.mathworks.com/help/images/examples/correcting-nonuniform-illumination.html .

2015 (2)

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

2010 (1)

J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
[Crossref] [PubMed]

2003 (2)

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
[Crossref] [PubMed]

2002 (1)

J. C. Olivo-Marin, “Extraction of spots in biological images using multiscale products,” Pattern Recognit. 35(9), 1989–1996 (2002).
[Crossref]

2000 (1)

J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit. 33(2), 225–236 (2000).
[Crossref]

1997 (1)

D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
[Crossref] [PubMed]

1986 (1)

1893 (1)

A. Köhler, “Ein neues Beleuchtungsverfahren für mikrophotographische Zwecke,” Z. Wiss. Mikrosk. 10, 433–440 (1893).

Brady, M.

F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
[Crossref] [PubMed]

Brainard, D. H.

Deshpande, S.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Elad, M.

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

Engstler, M.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Hochstetter, A.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Jiang, Z.

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

Jobson, D. J.

D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
[Crossref] [PubMed]

Keshet, R.

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

Kimmel, R.

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

Köhler, A.

A. Köhler, “Ein neues Beleuchtungsverfahren für mikrophotographische Zwecke,” Z. Wiss. Mikrosk. 10, 433–440 (1893).

Leong, F. J. W.-M.

F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
[Crossref] [PubMed]

Lu, Y.

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

McGee, J. O.

F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
[Crossref] [PubMed]

Meng, R.

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

Morel, J. M.

J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
[Crossref] [PubMed]

Olivo-Marin, J. C.

J. C. Olivo-Marin, “Extraction of spots in biological images using multiscale products,” Pattern Recognit. 35(9), 1989–1996 (2002).
[Crossref]

Petro, A. B.

J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
[Crossref] [PubMed]

Pfohl, T.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Pietikäinen, M.

J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit. 33(2), 225–236 (2000).
[Crossref]

Rahman, Z.

D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
[Crossref] [PubMed]

Sauvola, J.

J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit. 33(2), 225–236 (2000).
[Crossref]

Sbert, C.

J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
[Crossref] [PubMed]

Shaked, D.

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

Sobel, I.

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

Stellamanns, E.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Uppaluri, S.

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Wandell, B. A.

Woodell, G. A.

D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
[Crossref] [PubMed]

Wu, Y.

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

Xie, F.

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

IEEE Signal Process. Lett. (1)

Y. Lu, F. Xie, Y. Wu, Z. Jiang, and R. Meng, “No Reference Uneven Illumination Assessment for Dermoscopy Images,” IEEE Signal Process. Lett. 22(5), 534–538 (2015).
[Crossref]

IEEE Trans. Image Process. (2)

D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Trans. Image Process. 6(3), 451–462 (1997).
[Crossref] [PubMed]

J. M. Morel, A. B. Petro, and C. Sbert, “A PDE formalization of Retinex theory,” IEEE Trans. Image Process. 19(11), 2825–2837 (2010).
[Crossref] [PubMed]

Int. J. Comput. Vis. (1)

R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A Variational Framework for Retinex,” Int. J. Comput. Vis. 52(1), 7–23 (2003).
[Crossref]

J. Clin. Pathol. (1)

F. J. W.-M. Leong, M. Brady, and J. O. McGee, “Correction of uneven illumination (vignetting) in digital microscopy images,” J. Clin. Pathol. 56(8), 619–621 (2003).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (1)

Lab Chip (1)

A. Hochstetter, E. Stellamanns, S. Deshpande, S. Uppaluri, M. Engstler, and T. Pfohl, “Microfluidics-based single cell analysis reveals drug-dependent motility changes in trypanosomes,” Lab Chip 15(8), 1961–1968 (2015).
[Crossref] [PubMed]

Pattern Recognit. (2)

J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,” Pattern Recognit. 33(2), 225–236 (2000).
[Crossref]

J. C. Olivo-Marin, “Extraction of spots in biological images using multiscale products,” Pattern Recognit. 35(9), 1989–1996 (2002).
[Crossref]

Z. Wiss. Mikrosk. (1)

A. Köhler, “Ein neues Beleuchtungsverfahren für mikrophotographische Zwecke,” Z. Wiss. Mikrosk. 10, 433–440 (1893).

Other (3)

P. Nordenfelt, J. M. Cooper, and A. Hochstetter, “Matrix-masking to balance nonuniform illumination in microscopy,” https://nordlab.med.lu.se/?page_id=34 .

T. Ferreira and W. Rasband, ImageJ User Guide, ImageJ/Fij (2012).

The MathWorks Inc, “Correcting Nonuniform Illumination,” https://se.mathworks.com/help/images/examples/correcting-nonuniform-illumination.html .

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1
Fig. 1 Decision tree of which image processing could be chosen, depending on what background data is available.
Fig. 2
Fig. 2 The process of balancing uneven illumination with a background image of the same optical setup: (a) the original sample image, taken in a bright field setup of Koehler illumination, with obvious vignetting and a sample of high optical density. (b) background image, acquired using the same illumination with a sample-free stage. The brightness levels are evident to an uneven illumination with the brightest point to the left of the center, exhibiting a 2-dimensional normal distribution. (c) & (d) the sample image and the background image with normalized brightness, as interim results of the process. (e) interim result of subtracting the normalized background image (d) from the normalized sample image (c). (f) The final result, where the contrast has also been maximized. The sample now appears on a completely evenly lit background while all the details within the sample have been retained. (g) Graph depicting the grey value along the top-left to bottom-right diagonal (magenta line) of the original sample image [Fig. 1(a), top, black], the background image [Fig. 1(b), top, grey], and the resulting image [Fig. 1(f), bottom, black]. We chose to use a diagonal line for the image analysis to include both the center and the edges of the image, as well as a section of the sample and sample free parts of the image. Results using MATLAB and Python are essentially the same (see Fig. 5 for MATLAB results, Python results are shown here).
Fig. 3
Fig. 3 The process of extracting the background image of an uneven, yet symmetrically illuminated image. (a) the original image of a solution of fluorescent rhodamine B in a microfluidic channel [10]. (b) the extracted grey value along the brightest lines of the image’s long dimension, smoothed to avoid artefacts. (c) radially symmetric illumination matrix, which was obtained by multiplying the values shown in Fig. 3(b) with themselves. This is a reconstruction of the spatial intensity of the illumination source. (d) The creation of a matrix mask which can be used to cancel out the uneven illumination of the light source, fitted to the original image. (e) The resulting image of applying the mask to the original image. This image shows how the original image would have looked, had the light source had a perfectly even spatial light distribution. Results using MATLAB and Python are essentially the same (see Fig. 6 for MATLAB results, Python results are shown here).
Fig. 4
Fig. 4 Balancing of nonuniform illumination using the recommended procedure for MATLAB [13] for the same fluorescence image as in Fig. 2. (a) the original image of fluorescent rhodamine B in a microfluidic channel [10]. (b) The background image that was calculated by MATLAB. (c) The extracted grey value along the brightest lines of the original image (green, [Fig. 3(a)]) and of the corrected illumination image (black, [Fig. 3(e)]), analogous to Fig. 3(b). (d) The image, which results from subtracting the background image Fig. 4(b) from sample image Fig. 4(a). (e) The final result after maximizing the contrast. (f) The grey values along the same lines as above for Figs. 4(e) (black) and 4(a) (green) for comparison.
Fig. 5
Fig. 5 The figure shows MATLAB processing results of Fig. 2(a), comparable to the python processing shown in Fig. 2. (a) before (b) after the processing. (c) & (d) are pseudo-colored versions of (a) and (b).
Fig. 6
Fig. 6 The figure shows MATLAB processing results of Fig. 3(a), comparable to the python processing shown in Figs. 3(a) and 3(e). (a) before (b) after the processing. (c) & (d) are pseudo-colored versions of (a) and (b).

Tables (4)

Tables Icon

Table 1 General MATLAB and Python scripts for illumination balancing of brightfield images

Tables Icon

Table 2 Algorithm to obtain background image from a single grey-value line measurement

Tables Icon

Table 3 General MATLAB and Python scripts for illumination balancing of fluorescence images

Tables Icon

Table 4 MATLAB and python scripts for worse matrix approaches to illumination balancing

Metrics