Abstract
The automated image analysis is a powerful methodology for quantification microscopic images of living cells. But the proper and suitable indication of cells’ body in various kinds of microscopic images of cells is still not easy to perform. In this paper the methodology how to construct artificial images simulating bright field microscopic images is introduced. Using the adjusted and simplified version of software SIMCEP, prepared by Lehmussola and coworkers, proposed methodology is implemented and validated.
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Alberts, B., Bray, D., Lewis, J., Raf, M., Roberts, K., Watson, J.D.: Molecular Biology of the Cell, 3rd edn. Garland Publishing Inc., New York (1994)
Altman, D.G., Bland, J.M.: Diagnostic Tests. 1: Sensitivity and Specificity. British Medical Journal 11 (1994)
Buzanska, L., Jurga, M., Stachowiak, E.K., Stachowiak, M.K., Domanska-Janik, K.: Stem Cell and Development, vol. 15, pp. 391–406 (2006)
Buzanska, L., Machaj, E.K., Zabłocka, B., Podja, Z., Domanska-Janik, K.: Human cord blood-derived cells attain neuronal and glial features in vitro. J. Cell Sci. 115, 2131–2138 (2002)
Boezeman, B., Raymakers, R., Vierwinden, G., Linssen, P.: Automatic analysis of growth onset, growth rate and colony size of individual bone marrow progenitors. Cytometry 28, 305–310 (1997)
Czajkowska, J., Badura, P., Pietka, E.: 4D Segmentation of Ewing’s Sarcoma in MR Images. In: Piętka, E., Kawa, J. (eds.) Information Technologies in Biomedicine. AISC, vol. 69, pp. 91–100. Springer, Heidelberg (2010)
Czajkowska, J., Pietka, E., Kieltyka, A.: Multilevel Segmentation of Ewing’s Sarcoma Tumours in MR Images. Int. J. of Computer Assisted Radiology and Surgery 5, 344–345 (2010)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Hoppe, A., Korzynska, A., Wertheim, D.: A computer system for the analysis of neutrophil movement. Med. Biol. Eng. Comput. 37, 1000–1001 (1999)
Iwanowski, M., Korzynska, A.: Detection of the Area Covered by Neural Stem Cells in Cultures Using Textural Segmentation and Morphological Watershed. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 543–557. Springer, Heidelberg (2009)
Iwanowski, M., Korzyńska, A.: Segmentation of Moving Cells in Bright Field and Epi-Fluorescent Microscopic Image Sequences. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 401–410. Springer, Heidelberg (2010)
Kaufman, H., Tekalp, M.: Survey of estimation techniques in image restoration. IEEE Contr. Syst. 11(1), 16–24 (1991)
Korzynska, A.: Automatic Counting of Neural Stem Cells Growing in Cultures. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 2. ASC, vol. 45, pp. 604–612. Springer, Heidelberg (2007)
Korzynska, A., Iwanowski, M., Neuman, U., Dobrowolska, E., Hoser, P.: Comparison of the Methods of Microscopic Image Segmentation. In: Dössel, O., Schlegel, W.C. (eds.) WC 2009. IFMBE Proc., vol. 25/IV, pp. 425–428. Springer, Heidelberg (2009)
Korzynska, A., Jurga, M., Domanska-Janik, K., Strojny, W., Wloskowicz, D.: Analysis of Stem Cell Clonal Growth. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems. ASC, vol. 1, pp. 577–584. Springer, Heidelberg (2005)
Korzynska, A., Strojny, W., Hoppe, A., Wertheim, D., Hoser, P.: Segmentation of microscope images of living cells. Pattern. Anal. Appl. 10, 301–319 (2007)
Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational Framework for Simulating Fluorescece Microscope Images With Cell Population. IEEE Tran. on Med. Imaging 26(7) (2007)
Lehmussola, A., Selinummi, J., Ruusuvuori, P., Niemisto, A., Yli-Harja, O.: Simulating fluorescent microscope images of cell populations. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China (2005)
Lewinska, D., Bukowski, J., Kozuchowski, M., Kinasiewicz, A., Werynsk, A.: Electrostatic microencapsulation of living cells. Biocybern. Biomed. Eng. 28, 69–84 (2008)
Liao, Q., Deng, Y.: An accurate segmentation method for white blood cell images. In: Proc. Int. Symp. on Biomedical Imaging, pp. 245–248 (2002)
Markiewcz, T., Osowski, S.: Morphological operations for blood cells extraction from the image of the bone marrow smear. Prz. Elektrotechniczn. 84, 24–26 (2008)
Markiewicz, T., Wisniewsk, P., Osowski, S., Patera, J., Kołowski, W., Koktysz, R.: Comparative analysis of methods for accurate recognition of cells through nuclei staining of KI-67 in neuroblastoma and estrogen/progesteronestatus staining in breast cancer. Anal. Quant. Cytol. Histol. 31, 49–62 (2009)
Neuman, U., Korzynska, A., Lopez, C., Lejeun, M.: Segmentation of Stained Lymphoma Tissue Section Images. In: Pieta, E., Kawa, J. (eds.) Information Technology in Biomedicine 2. ASC, vol. 69, pp. 101–113. Springer, Heidelberg (2010)
Neuman, U., Korzynska, A., Lopez, C., Leujene, M., Bosch, R.: Intensity correction of the immunohistochmically stained tessue sections images influence on results of segmentation accurence. In: TPO 2010 Conference Proceedings, pp. 35–40 (2010) (in polish)
Neuman, U., Korzynska, A., Lopez, C., Leujene, M., Bosch, R.: Equalisation of archival microscopic images from immunohistochmically stained tissue sections. Accepted for Biocyb. and Biomed. Eng. (2012)
Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V., Beksac, M., Beksak, S.: An automated differential blood count system. P. Ann. Int. IEEE EMBS 3, 2583–2586 (2001)
Perlin, K.: An image synthetizer. Comput. Graph. 19, 287–296 (1985)
Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–338 (2000)
Proffitt, R., Tran, J.V., Reynolds, C.P.: A fluorescence digital image microscopy system for quantifying relative cell numbers in tissue culture plates. Cytometry 24, 204–213 (1996)
Russ, J.C.: Image Processing Handbook, 4th edn. CRC Press, Tokyo (2002)
Selinummi, J., Ruusuvuori, P., Podolsky, P., Ozinsky, I.A., Gold, E., Yli-Harja, O., Aderem, A., Shmulevich, I.: Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images. PLoS One 4(10), e7497 (2009)
Solorzano, C.O., Rodgirguez, E.G., Jone, S.A., Pinkel, J., Gray, J.W.: Segentation of Confocal Microscope Images of Cell Nuclei in Thick Tissue Section. J. Microsc. 193, 212–226 (1999)
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Korzynska, A., Iwanowski, M. (2012). Artifical Images for Evaluation of Segmentation Results: Bright Field Images of Living Cells. In: Piętka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Lecture Notes in Computer Science(), vol 7339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31196-3_45
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DOI: https://doi.org/10.1007/978-3-642-31196-3_45
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