Analyses of DNA Image Cytometry Uncertainty Caused by Diffractive Blurring

Abstract:

Comparison of quantitative methods for the segmentation of nuclei images is performed. A sizeable variability is typical for biological specimens and it induces the elemental uncertainty in experimental data. To remove the variability from comparison we have proposed and built the special test objects of two types simulating the nuclei images. I-type objects are test patterns as sets of circles and squares of specified dimensions in chromium films on a glass plate. II-type objects are images with brightness differentials that simulate diffractive blurring and are built up with MathCad programming environment. Test objects enable objective comparison of characteristics obtained in the course of their quantitative optical-and-structural analysis using various algorithms and programs. We found that the efficiency of the segmentation algorithm depends on diffractive blurring of the image. Specifics of Otsu’s algorithm and local algorithm of brightness gradient are analyzed for finding the segmentation threshold of digital images modeling transmission Feulgen-stained cell nuclei specimens with diffractive blurring. The performed calculations revealed that the border of geometrooptical image practically coincide with the points of inflection on the intensity distribution graph in a test-object image space. Computational experiments show that quantitative results of the morphometric image study defined by the various segmentation algorithms vary within 5%. It is established that the threshold-identifying algorithm based on the brightness gradient is preferable in the image cytometry.

• Periodical: Applied Mechanics and Materials (Volume 870)
• Pages: 369-374
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.870.369
• Online since: September 2017
• Authors: Irina G. Palchikova*, Evgenii S. Smirnov, Alexander A. Konev
• Keywords: Brightness Gradient, Diffractive Uncertainty, Digital Image, Fresnel Diffraction, Microscope, Otsu’s Algorithm, Segmentation

References

[1] L. Agroskin, G. Papayan, Cytophotometry. Equipment and methods for cell light absorbance analysis, Leningrad, 1977 (In Russian).

[2] M. Puech, F. Giroud, Standardization of DNA quantitation by image analysis: quality control of instrumentation, Cytometry 36 (1999) 11-17.

DOI: 10.1002/(sici)1097-0320(19990501)36:1<11::aid-cyto2>3.0.co;2-t

[3] I. G. Palchikova, L. V. Omelyanchuk, E. I. Palchikov, E. S. Smirnov, V. F. Semeshin, Specifics of using digital cameras for cytophotometric measurement of DNA quantity in cell nuclei, Sensors, and systems 3(2012) 2-12 (In Russian).

[4] I. G. Palchikova, E. V. Ivankina, V. F. Semeshin, L. V. Omelyanchuk, I. F. Zhimulev, E. S. Smirnov, DNA Feulgen cytophotometry and chromatin diminution, Int. Multidisciplinary Microscopy Congress (2014) 233-239.

DOI: 10.1007/978-3-319-04639-6_33

[5] D. Nyyssonen, Linewidth measurement with an optical microscope: the effect of operating conditions on the image profile, Applied Optical 16(1977) 2223-2230.

DOI: 10.1364/ao.16.002223

[6] G. Haroske, W. D. Meyer, F. Theissig., K D Kunze, Increase of precision and accuracy of DNA cytometry by correcting diffraction and glare errors, Analytical cellular pathology 9(1995) 1-12.

[7] I. G. Palchikova, L. V. Omelyanchuk, E. S. Smirnov, Effect of diffraction on the results of quantitative cytophotometry, Optoelectronics, Instrumentation and Data Processing 48(2012) 612-619.

DOI: 10.3103/s875669901206009x

[8] N. A. Otsu, Threshold selection method from gray-level histograms, IEEE Transactions on Systems Man and Cybernetics 9(1979) 62-66.

DOI: 10.1109/tsmc.1979.4310076

[9] Rafael C Gonzalez and R E Woods, Digital image processing, Pearson Education Inc., Prentice Hall (2008).