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BioNanoScience

, Volume 3, Issue 4, pp 403–414 | Cite as

Empowering Low-Cost CMOS Cameras by Image Processing to Reach Comparable Results with Costly CCDs

  • Gözen Köklü
  • Julien Ghaye
  • Ralph Etienne-Cummings
  • Yusuf Leblebici
  • Giovanni De Micheli
  • Sandro Carrara
Article

Abstract

Despite the huge research effort to improve the performance of the complementary metal oxide semiconductor (CMOS) image sensors, charge-coupled devices (CCDs) still dominate the cell biology-related conventional fluorescence microscopic imaging market where low or ultra-low noise imaging is required. A detailed comparison of the sensor specifications and performance is usually not provided by the manufacturers which leads the end users not to go out of the habitude and choose a CCD camera instead of a CMOS one. However, depending on the application, CMOS cameras, when empowered by image processing algorithms, can become cost-efficient solutions for conventional fluorescence microscopy. In this paper, we introduce an application-based comparative study between the default CCD camera of an inverted microscope (Nikon Ti-S Eclipse) and a custom-designed CMOS camera and apply efficient image processing algorithms to improve the performance of CMOS cameras. Quantum micro-bead samples (emitting fluorescence light at different intensity levels), breast cancer diagnostic tissue cell samples, and Caco-2 cell samples are imaged by both CMOS and CCD cameras. The results are provided to show the reliability of CMOS camera processed images and finally to be of assistance when scientists select their cameras for desired applications.

Keywords

Fluorescence microscopy cameras CMOS camera CCD camera CCD vs CMOS CMOS image sensor CCD image sensor 

Notes

Acknowledgments

The research work presented in this paper was funded by the NutriCHIP project with a grant from the Swiss Nano-Tera.ch initiative, evaluated by the Swiss National Science Foundation. It was also partially supported by the NanoSys project, in the program ERC-2009-AdG-246810. Finally, the authors would like to thank to Ata Tuna Çiftlik from LMIS2 (Microsystems Laboratory 2), EPFL, for their support in tissue sample preparation; and Ali Galip Bayrak from LAP (Processor Architecture Laboratory), EPFL, for his precious suggestions and the useful discussions.

References

  1. 1.
    Fossum, E.R., Hynecek, J., Tower, J., Teranishi, N., Nakamura, J., Magnan, P., Theuwissen, A. (2009). Special issue on solid-state image sensors. IEEE Transactions on Electron Devices, 56(11), 2376–2379.CrossRefGoogle Scholar
  2. 2.
    Ansorge, W.J. (2009). Next-generation DNA sequencing techniques. New Biotechnology, 25(4), 195–203.CrossRefGoogle Scholar
  3. 3.
    Sapuppo, F., Intaglietta, M., Bucolo, M. (2008). Bio-microfluidics real-time monitoring using CNN technology. IEEE Transactions on Biomedical Circuits and Systems, 2(2), 78–87.CrossRefGoogle Scholar
  4. 4.
    Kang, H.W., Muramatsu, H., Lee, B.J., Kwon, Y.S. (2010). Monitoring of anticancer effect of cisplatin and 5-fluorouracil on HepG2 cells by quartz crystal microbalance and micro CCD camera. Biosensors and Bioelectronics, 26(4), 1576–1581.CrossRefGoogle Scholar
  5. 5.
    Osmani, N., Peglion, F., Chavrier, P., Etienne-Manneville, S. (2010). Cdc42 localization and cell polarity depend on membrane traffic. The Journal of Cell Biology, 191(7), 1261–1269.CrossRefGoogle Scholar
  6. 6.
    Nicolini, C., Carrara, S., Mascetti, G. (1997). High order DNA structure as inferred by optical fluorimetry and scanning calorimetry. Molecular Biology Reports, 24(4), 235–246.CrossRefGoogle Scholar
  7. 7.
    Mascetti, G., Vergani, L., Diaspro, A., Carrara, S., Radicchi, G., Nicolini, C. (1998). Effect of fixatives on calf thymocytes chromatin as analyzed by 3D high-resolution fluorescence microscopy. Cytometry, 23(2), 110–119.CrossRefGoogle Scholar
  8. 8.
    Mascetti, G., Carrara, S., Vergani, L. (2001). Relationship between chromatin compactness and dye uptake for in situ chromatin stained with DAPI. Cytometry, 44(2), 113–119.CrossRefGoogle Scholar
  9. 9.
    Fossum, E. R. (1993). Active pixel sensors: are CCD’s dinosaurs? In Proceedings SPIE (Vol. 1900, pp. 1–3).Google Scholar
  10. 10.
    Fossum, E. R. (1997). CMOS image sensors: electronic camera-on-chip. IEEE Transactions Electron Devices, 44, 1689–1698.CrossRefGoogle Scholar
  11. 11.
    Murari, K., Etienne-Cummings, R., Cauwenberghs, G., Thakor, N. (2010). An integrated imaging microscope for untethered cortical imaging in freely-moving animals. In Engineering in medicine and biology society (EMBC), 2010 annual international conference of the IEEE (pp. 5795–5798).Google Scholar
  12. 12.
    Murari, K., Greenwald, E., Etienne-Cummings, R., Cauwenberghs, G., Thakor, N. (2009). Design and characterization of a miniaturized epi-illuminated microscope. In Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE (pp. 5369–5372).Google Scholar
  13. 13.
    Ghosh, K.K., Burns, L.D., Cocker, E.D., Nimmerjahn, A., Ziv, Y., El Gamal, A., Schnitzer, M.J. (2011). Miniaturized integration of a fluorescence microscope. Nature Methods, 8(10), 871–878.CrossRefGoogle Scholar
  14. 14.
    Li, D.U., Arlt, J., Richardson, J., Walker, R., Buts, A., Stoppa, D., Henderson, R. (2010). Real-time fluorescence lifetime imaging system with a 32 × 320.13μm CMOS low dark-count single-photon avalanche diode array. Optics Express, 18(10), 10257–10269.CrossRefGoogle Scholar
  15. 15.
    Schwartz, D.E., Charbon, E., Shepard, K.L. (2008). A single-photon avalanche diode array for fluorescence lifetime imaging microscopy. IEEE Journal of Solid-State Circuits, 43(11), 2546–2557.CrossRefGoogle Scholar
  16. 16.
    Mutch, S.A., et al. (2007). Deconvolving single-molecule intensity distributions for quantitative microscopy measurements. Biophysical Journal, 92.8, 2926–2943.CrossRefGoogle Scholar
  17. 17.
    Cronin, B., de Wet, B., Wallace, M.I. (2009). Lucky imaging: improved localization accuracy for single molecule imaging. Biophysical Journal, 96.7, 2912–2917.CrossRefGoogle Scholar
  18. 18.
    Ghaye, J., De Micheli, G., Carrara, S. (2012). Simulated biological cells for receptor counting in fluorescence imaging. BioNanoScience, 2.2, 94–103.CrossRefGoogle Scholar
  19. 19.
    Favi, C., Beuchat, R., Jimenez, X., Ienne, P. (2009). From gates to multi-processors learning systems hands-on with FPGA4U in a computer science programme. In Proceedings of the 2009 workshop on embedded systems security (WESS). Grenoble.Google Scholar
  20. 20.
    PointGrey Research (2010). Technical application note TAN2008006, Richmond. http://www.ptgrey.com/support/downloads/documents_/TAN2008006_Sensor_Response_Curve_Comparison_for_ICX445.pdf. Accessed 2 Jul 2012.
  21. 21.
    Aptina Imaging Corporation (2006). 1/3-inch wide-VGA CMOS digital image sensor, San Jose. http://www.aptina.com/products/image_sensors/mt9v032d00stm/. Accessed 2 Jul 2012.
  22. 22.
    Lumenera Corporation (2008). Infinity X-32M, 32 megapixel CCD USB 2.0 camera. http://www.emsdiasum.com/microscopy/technical/datas_heet/95116_95117.pdf. Accessed 2 Jul 2012.
  23. 23.
    Lumenera Corporation (2010). Infinity 1-1M, 1.3 megapixel monochrome camera. http://www.oem-optical.com/lumenera-cmos-1model.html. Accessed 2 Jul 2012.
  24. 24.
    Theuwissen, A. (2012). Digital imaging: image capturing, image sensors—technologies and applications. In Annual international courses in telecommunications semiconductor technology nanotechnology, CEI-Europe. Barcelona.Google Scholar
  25. 25.
    Aptina Imaging Corporation (2012). 1/2.5-Inch 5MP CMOS digital image sensor, San Jose. http://www.aptina.com/products/image_sensors/mt9p031i12stm/. Accessed 2 Jul 2012.
  26. 26.
    Aptina Imaging Corporation (2012). 1 Megapixel 1/3-inch digital image sensor Recon and iLCC, San Jose. http://www.aptina.com/products/image_sensors/ar0130cs. Accessed 2 Jul 2012.
  27. 27.
    Johansson, R., Storm, A., Stephansen, C., Eikedal, S., Willassen, T., Skaug, S., Perks, D. (2011). A 1/13-inch 30fps VGA SoC CMOS image sensor with shared reset and transfer-gate pixel control. In Solid-state circuits conference digest of technical papers (ISSCC), 2011 IEEE international (pp. 414–415).Google Scholar
  28. 28.
    Seo, M.W., Suh, S.H., Iida, T., Takasawa, T., Isobe, K., Watanabe, T., Kawahito, S. (2012). A low-noise high intrascene dynamic range CMOS image sensor with a 13 to 19b variable-resolution column-parallel folding-integration/cyclic ADC. IEEE Journal of Solid-State Circuits, 47(1), 272–283.CrossRefGoogle Scholar
  29. 29.
    Koklu, G., Ghaye, J., Beuchat, R., De Micheli, G., Leblebici, Y., Carrara, S. (2012). Quantitative comparison of commercial CCD and custom-designed CMOS camera for biological applications. In 2012 IEEE international symposium on circuits and systems (ISCAS) (pp. 2063–2066).Google Scholar
  30. 30.
    Bigas, M., Cabruja, E., Forest, J., Salvi, J. (2006). Review of CMOS image sensors. Microelectronics Journal, 37(5), 433–451.CrossRefGoogle Scholar
  31. 31.
    Schöberl, M., Senel, C., Fössel, S., Bloss, H., Kaup, A. (2009). Non-linear dark current fixed pattern noise compensation for variable frame rate moving picture cameras. In Proceedings 17th European signal processing conference (EUSIPCO) (pp. 268–272).Google Scholar
  32. 32.
    Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27.Google Scholar
  33. 33.
    Song, B., Sivagnanam, V., Vandevyver, C.D., Hemmila, I., Lehr, H.A., Gijs, M.A., Bünzli, J.C.G. (2009). Time-resolved lanthanide luminescence for lab-on-a-chip detection of biomarkers on cancerous tissues. Analyst, 134(10), 1991–1993.CrossRefGoogle Scholar
  34. 34.
    Ciftlik, A.T., Song, B., Vandevyver, C., Bünzli, J.C., Lehr, H.A., Gijs, M. (2010). Fast immunohistochemical biomarker detection device for cancer tissue slices. In Proceedings of 14th international conference on miniaturized systems for chemistry and life sciences (MicroTAS) (pp. 699–70).Google Scholar
  35. 35.
    Hilgers, A.R., Conradi, R.A., Burton, P.S. (1990). Caco-2 cell monolayers as a model for drug transport across the intestinal mucosa. Pharmaceutical Research, 7.9, 902–910.CrossRefGoogle Scholar
  36. 36.
    Vergeres, G., & et al. (2012). The NutriChip project translating technology into nutritional knowledge. British Journal of Nutrition, 1.1, 1–7.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gözen Köklü
    • 1
    • 2
  • Julien Ghaye
    • 1
  • Ralph Etienne-Cummings
    • 3
  • Yusuf Leblebici
    • 2
  • Giovanni De Micheli
    • 1
  • Sandro Carrara
    • 1
  1. 1.Integrated Systems Laboratory (LSI)Swiss Federal Institute of TechnologyLausanneSwitzerland
  2. 2.Microelectronic Systems Laboratory (LSM)Swiss Federal Institute of TechnologyLausanneSwitzerland
  3. 3.Computational Sensory-Motor Systems LabJohns Hopkins UniversityBaltimoreUSA

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