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Comparison and Validation of Visual Assessment and Image Processing Algorithms to Quantify Morphology Dynamics of Euglena gracilis

Published online by Cambridge University Press:  30 July 2012

Anand Krishnan ,
Ian Watson ,
Roger Parton  and
James Sharp
Anand Krishnan
Affiliation:
Systems, Power and Energy, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Ian Watson*
Affiliation:
Systems, Power and Energy, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Roger Parton
Affiliation:
Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8QQ, UK
James Sharp
Affiliation:
Systems, Power and Energy, School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
*
Corresponding author. E-mail: ian.watson@glasgow.ac.uk
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Abstract

Image processing algorithms were developed and compared with visual assessment from 12 volunteers to quantify the temporal morphological structure of a single Euglena gracilis organism. Representative images of E. gracilis, showing different morphological characteristics from ovate to cylindrical and elongate, were captured with a bright-field video microscopy system. These images were ranked by the volunteers in order from ovate to elongate. The images were analyzed in the spatial and spatial frequency domain, and the order of the images from each analysis was ranked against the visual assessment. The assessment methods agreeing with the volunteer's preferred sequence were an eccentricity measurement (major axis over the sum of the minor axis at three points), the cross correlation of the image without high pass filtering or edge detection, and cross correlation of the power spectral density.

Keywords

visual assessmentmorphologyimage processingpower spectral densityintensity standard deviationpixel intensityEuglena gracilis
Type
Biological Applications: Techniques, Software, and Equipment Development
Information
Microscopy and Microanalysis , Volume 18 , Issue 4 , August 2012 , pp. 798 - 807
Copyright
Copyright © Microscopy Society of America 2012

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Footnotes

Current address: Coherent, Inc., West of Scotland Science Park, Glasgow G20 0XA, UK

References

REFERENCES

Ascoli, C., Barbi, M., Frediani, C. & Mure, A. (1978a). Measurements of Euglena motion parameters by laser light scattering. Biophys J 24, 585–599.CrossRefGoogle ScholarPubMed
Ascoli, C., Barbi, M., Frediani, C. & Petracchi, D. (1978b). Effects of electromagnetic fields on the motion of Euglena gracilis . Biophys J 24(3), 601–612.CrossRefGoogle ScholarPubMed
Brain, R.A., Johnson, D.J., Richards, S.M., Sanderson, H., Sibley, P.K. & Caillol, J.M. (2004). Some applications of the Lambert W function to classical statistical mechanics. J Phys A-Math Gen 36, 10431–10442.Google Scholar
Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2004). Digital Image Processing Using Matlab. Upper Saddle River, NJ: Pearson Education, Inc. Google Scholar
Hader, D.P., Lebert, M. & Richter, P. (1998). Gravitaxis and graviperception in Euglena gracilis . Adv Space Res 21(8-9), 1277–1284.CrossRefGoogle ScholarPubMed
Kim, B., Lee, H., Kim, K.J., Seo, J., Park, S., Shin, Y.G., Kim, S.H. & Lee, K.H. (2011). Comparison of three image comparison methods for the visual assessment of the image fidelity of compressed computed tomography images. Med Phys 38(2), 836–844.CrossRefGoogle ScholarPubMed
Lee, J.J., Leedale, G.F. & Bradbury, P. (Eds.) (2000). An Illustrated Guide to the Protozoa, 2nd ed. Society of Protozoologists. Lawrence, KS: Allen Press.Google Scholar
Lonergan, T.A. & Lachney, C.I. (1985). Regulation of cell shape in Euglena gracilis. I. Involvement of stable microtubules. J Cell Sci 74(1), 219–237.Google Scholar
Martin, W. & Borst, P. (2003). Secondary loss of chloroplasts in trypanosomes. Proc Natl Acad Sci 100(3), 765–767.CrossRefGoogle ScholarPubMed
McNeill, A.R. (1979). The Invertebrates. London: Cambridge University Press.Google Scholar
Nascimento, V.H. & Sayed, A.H. (2000). On the learning mechanism of adaptive filters. IEEE Trans Sign Proc 48, 1609–1625.CrossRefGoogle Scholar
Roy, P.S. & Joshi, P.K. (2002). Forest cover assessment in north-east India—The potential of temporal wide swath satellite sensor data (IRS-1C WiFS). Int J Remote Sensing 23(22), 4881–4896.CrossRefGoogle Scholar
Van Der Bilt, A., Speksnijder, C.M., De Liz Pocztaruk, R. & Abbink, J.H. (2012). Digital image processing versus visual assessment of chewed two-colour wax in mixing ability tests. J Oral Rehab 39(1), 11–17.CrossRefGoogle ScholarPubMed
Vanha-Majamaa, I., Salemaa, M., Tuominen, S. & Mikkola, K. (2000). Digitized photographs in vegetation analysis: A comparison of cover estimates. Appl Veg Sci 3, 89–94.CrossRefGoogle Scholar