Advertisement

Improving Secondary Ion Mass Spectrometry Image Quality with Image Fusion

  • Jay G. Tarolli
  • Lauren M. Jackson
  • Nicholas Winograd
Research Article

Abstract

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images but also by detection sensitivity. As the probe size is reduced to below 1 μm, for example, a low signal in each pixel limits lateral resolution because of counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure.

Key words

SIMS Image processing Image fusion Algae Biofuels Botryococcus braunii 

Notes

Acknowledgments

The authors acknowledge financial support from the National Institute of Health under grant no. 5R01 EB002016-19, and the Department of Energy under grant no. DE-FG-02-06ER15803. The authors thank Richard Caprioli for suggesting the use of image fusion in SIMS, as well as Jordan Lerach for preparing and Hua Tian for obtaining SEM and SIMS images of gold-coated grid samples.

References

  1. 1.
    Fletcher, J.S., Lockyer, N.P., Vickerman, J.C.: Molecular SIMS imaging; spatial resolution and molecular sensitivity: have we reached the end of the road? Is there light at the end of the tunnel? Surf. Interface Anal. 43, 253–256 (2011)CrossRefGoogle Scholar
  2. 2.
    Piehowski, P.D., Davey, A.M., Kurczy, M.E., Sheets, E.D., Winograd, N., Ewing, A.G., Heien, M.L.: Time-of-flight secondary ion mass spectrometry imaging of subcellular lipid heterogeneity: Poisson counting and spatial resolution. Anal. Chem. 81, 5593–5602 (2009)CrossRefGoogle Scholar
  3. 3.
    Henderson, A., Fletcher, J.S., Vickerman, J.C.: A comparison of PCA and MAF for ToF-SIMS image interpretation. Surf. Interface Anal. 41, 666–674 (2009)CrossRefGoogle Scholar
  4. 4.
    Tyler, B.J., Rayal, G., Castner, D.G.: Multivariate analysis strategies for processing ToF-SIMS images of biomaterials. Biomaterials 28, 2412–2423 (2007)CrossRefGoogle Scholar
  5. 5.
    Tyler, B.: Interpretation of TOF-SIMS images: multivariate and univariate approaches to image de-noising, image segmentation and compound identification. Appl. Surf. Sci. 203, 825–831 (2003)CrossRefGoogle Scholar
  6. 6.
    Wickes, B.T., Kim, Y., Castner, D.G.: Denoising and multivariate analysis of time-of-flight SIMS images. Surf. Interface Anal. 35, 640–648 (2003)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Zhou, D., Armenakis, C., Li, D., Li, Q.: A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sensing 43, 1391–1402 (2005)CrossRefGoogle Scholar
  8. 8.
    Mumtaz, A., Majid, A., Mumtaz, A.: Genetic Algorithms and Its Application to Image Fusion. Proceedings of the International Conference on Emerging Technologies, Rawalpindi, Pakistan, October 18–19, 6–10 (2008)Google Scholar
  9. 9.
    Khan, A.M., Khan, A.: Fusion of visible and thermal images using support vector machines. Proceedings of the 10th IEEE International Multitopic Conference, Islamabad, Pakistan, December 23–24, 146–151 (2006)Google Scholar
  10. 10.
    Wen, C.Y., Chen, J.K.: Multi-resolution image fusion technique and its application to forensic science. Forensic Sci. Int. 140, 217–232 (2004)CrossRefGoogle Scholar
  11. 11.
    Ashoori, A., Moshiri, B., Setarehdan, S.K.: Fuzzy image fusion application in detecting coronary layers in IVUS pictures. S.K. Proceedings of the 3rd International Symposium on Communications, Control, and Signal Processing, St. Julian's, Malta, March 12–14, Vols 1/3, 20–24 (2008)Google Scholar
  12. 12.
    Rubio-Guivernau, J.L., Gurchenkov, V., Luengo-Oroz, M.A., Duloquin, L., Bourgine, P., Santos, A., Peyrieras, N., Ledesma-Carbayo, M.J.: Wavelet-based image fusion in multi-view three-dimensional microscopy. Bioinformatics 28, 238–245 (2012)CrossRefGoogle Scholar
  13. 13.
    Zhong, Z., Blum, R.S.: A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proc. IEEE 87, 1315–1326 (1999)CrossRefGoogle Scholar
  14. 14.
    Shivsubramani Krishnamoorthy, K.P.S.: Implemetation and comparative study of image fusion algorithms. Int. J. Comput. Appl. 9, 25–35 (2010)Google Scholar
  15. 15.
    Artyushkova, K., Pylypenko, S., Dowlapalli, M., Atanassov, P.: Use of digital image processing of microscopic images and multivariate analysis for quantitative correlation of morphology, activity, and durability of electrocatalysts. RSC Advances 2, 4304–4310 (2012)CrossRefGoogle Scholar
  16. 16.
    Artyushkova, K., Fulghum, J.E.: Multivariate image analysis methods applied to XPS imaging data sets. Surf. Interface Anal. 33, 185–195 (2002)CrossRefGoogle Scholar
  17. 17.
    Lloyd, K.G., Walls, D.J., Wyre, J.P.: Correlating data from multiple surface-specific techniques using multivariate methods: examples and considerations. Surf. Interface Anal. 41, 686–693 (2009)CrossRefGoogle Scholar
  18. 18.
    Rokni, K., Marghany, M., Hashim, M., Hazini, S.: Comparative statistical-based and color-related pan sharpening algorithms for ASTER and RADARSAT SAR satellite data. IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE), Penang, Malaysia, December 4–7, 618–622 (2011)Google Scholar
  19. 19.
    Artyushkova, K., Farrar, J.O., Fulghum, J.E.: Data fusion of XPS and AFM images for chemical phase identification in polymer blends. Surf. Interface Anal. 41, 119–126 (2009)CrossRefGoogle Scholar
  20. 20.
    Simpson, A.J., Zang, X., Kramer, R., Hatcher, P.G.: New insights on the structure of algaenan from Botryoccocus braunii race A and its hexane insoluble botryals based on multidimensional NMR spectroscopy and electrospray-mass spectrometry techniques. Phytochemistry 62, 783–796 (2003)CrossRefGoogle Scholar
  21. 21.
    Tanoi, T., Kawachi, M., Watanabe, M.M.: Effects of carbon source on growth and morphology of Botryococcus braunii. J. Appl. Phycol. 23, 25–33 (2011)CrossRefGoogle Scholar
  22. 22.
    Weiss, T.L., Chun, H.J., Okada, S., Vitha, S., Holzenburg, A., Laane, J., Devarenne, T.P.: Raman spectroscopy analysis of botryococcene hydrocarbons from the green microalga Botryococcus braunii. J. Biol. Chem. 285, 32458–32466 (2010)CrossRefGoogle Scholar
  23. 23.
    Padwick, C., Pacifici, F., Smallwood, S.: WorldView-2 Pan-Sharpening. Proceedings of the ASPRS Annual Conference, San Diego, California, US, April 26–30 (2010)Google Scholar
  24. 24.
    Fletcher, J.S., Rabbani, S., Henderson, A., Blenkinsopp, P., Thompson, S.P., Lockyer, N.P., Vickerman, J.C.: A new dynamic in mass spectral imaging of single biological cells. Anal. Chem. 80, 9058–9064 (2008)CrossRefGoogle Scholar
  25. 25.
    Pavlic, G., Singhroy, V., Duk-Rodkin, A., Alasset, P.J.: Satellite data fusion techniques for terrain and surficial geological mapping. Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE Int 3, 314 (2008)Google Scholar
  26. 26.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vision Comput 21, 977–1000 (2003)CrossRefGoogle Scholar
  27. 27.
    Manjusha Deshmukh, U.B.: Image fusion and image quality assessment of fused images. Int. J. Image Processing 4, 484–508 (2010)Google Scholar
  28. 28.
    Weiss, T.L., Roth, R., Goodson, C., Vitha, S., Black, I., Azadi, P., Rusch, J., Holzenburg, A., Devarenne, T.P., Goodenough, U.: Colony organization in the green alga Botryococcus braunii (Race B) is specified by a complex extracellular matrix. Eukaryotic Cell 11, 1424–1440 (2012)CrossRefGoogle Scholar
  29. 29.
    Oner, E.T.: Pretreatment Techniques for Biofuels and Biorefineries; Springer, Berlin, pp. 35–36 (2013)Google Scholar

Copyright information

© American Society for Mass Spectrometry 2014

Authors and Affiliations

  • Jay G. Tarolli
    • 1
  • Lauren M. Jackson
    • 1
  • Nicholas Winograd
    • 1
  1. 1.Department of ChemistryPennsylvania State UniversityUniversity ParkUSA

Personalised recommendations