Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Opto-Electronics Review

Editor-in-Chief: Jaroszewicz, Leszek

Open Access
See all formats and pricing
More options …
Volume 20, Issue 2


Multistage morphological segmentation of bright-field and fluorescent microscopy images

A. Korzyńska / M. Iwanowski
  • Institute of Control and Industrial Electronics, Warsaw University of Technology, 75 Koszykowa Str., 00-662, Warsaw, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2012-10-17 | DOI: https://doi.org/10.2478/s11772-012-0026-x


This paper describes the multistage morphological segmentation method (MSMA) for microscopic cell images. The proposed method enables us to study the cell behaviour by using a sequence of two types of microscopic images: bright field images and/or fluorescent images. The proposed method is based on two types of information: the cell texture coming from the bright field images and intensity of light emission, done by fluorescent markers. The method is dedicated to the image sequences segmentation and it is based on mathematical morphology methods supported by other image processing techniques. The method allows for detecting cells in image independently from a degree of their flattening and from presenting structures which produce the texture. It makes use of some synergic information from the fluorescent light emission image as the support information. The MSMA method has been applied to images acquired during the experiments on neural stem cells as well as to artificial images. In order to validate the method, two types of errors have been considered: the error of cell area detection and the error of cell position using artificial images as the “gold standard”.

Keywords: image segmentation; microscopic image of living cells; bright field images; fluorescent images; epi-fluorescent images; texture analysis; watershed

  • [1] L. Mirosław, A. Chorążyczewski, F. Buchholz, and R. Kittler, “Correlation-based method for automatic mitotic cell detection in phase contrast microscopy”, Adv. Soft Comp. 1, pp. 627–634, edited by M. Kurzyński, E. Puchała, M. Woźniak, and A. Żołnierek, Springer-Verlag, Berlin Heidelberg, 2005. Google Scholar

  • [2] R.T. Proffitt, J.V. Tran, and C.P. Reynolds, “A fluorescence digital image microscopy system for quantifying relative cell numbers in tissue culture plates”, Cytometry 24, 204–213 (1996). http://dx.doi.org/10.1002/(SICI)1097-0320(19960701)24:3<204::AID-CYTO3>3.0.CO;2-HCrossrefGoogle Scholar

  • [3] D. Lewińska, J. Bukowski, M. Kożuchowski, A. Kinasiewicz, and A. Weryński, “Electrostatic microencapsulation of living cells”, Biocybern. Biomed. Eng. 28, 69–84 (2008). Google Scholar

  • [4] J. Boezeman, R. Raymakers, G. Vierwinden, and P. Linssen, “Automatic analysis of growth onset, growth rate and colony size of individual bone marrow progenitors”, Cytometry 28, 305–310 (1997). http://dx.doi.org/10.1002/(SICI)1097-0320(19970801)28:4<305::AID-CYTO5>3.0.CO;2-ACrossrefGoogle Scholar

  • [5] A. Korzyńska, M. Jurga, K. Domańska-Janik, W. Strojny, and D. Włoskowicz, “Analysis of stem cell clonal growth”, Adv. Soft Comp., 1, pp. 577–584, edited by M. Kurzyński, E. Puchała, M. Woźniak, and A. Żołnierek, Springer-Verlag, Berlin Heidelberg, 2005. Google Scholar

  • [6] A. Korzyńska and M. Zychowicz “A method of estimation of the Cell Doubling time on Basis of the Cell culture Monitoring Data”, Biocybern. Biomed. Eng. 28, 75–82 (2008). Google Scholar

  • [7] K. Jiang, Q.M. Liao, and S.Y. Dai, “A novel white blood cell segmentation scheme using scale-space altering and watershed clustering”, Proc. Int. C. Machine Learning and Cybernetics 5, 2820–2825 (2003). Google Scholar

  • [8] Q. Liao and Y. Deng, “An accurate segmentation method for white blood cell images”, Proc. Int. Symp. on Biomedical Imaging, 245–248 (2002). Google Scholar

  • [9] B. Nilsson and A. Heyden, “Model-based segmentation of leukocyte clusters”, Proc. Int. C. Patt. Recog. 1, 727–730 (2002). Google Scholar

  • [10] G. Ongun, U. Halici, K. Leblebicioglu, V. Atalay, M. Beksac, and S. Beksak, “An automated differential blood count system”, P. Ann. Int. IEEE EMBS 3, 2583–2586 (2001). Google Scholar

  • [11] A. Hoppe, A. Korzyńska, and D. Wertheim “A computer system for the analysis of neutrophil movement”, Med. Biol. Eng. Comput. 37, 1000–1001 (1999). http://dx.doi.org/10.1007/BF02513323CrossrefGoogle Scholar

  • [12] P. Bartels, R. Montironi, V. Duval da Silva, P. Hamilton, D. Thompson, L. Vaught, and H.G. Bartels, “Tissue architecture analysis in prostate cancer and its precursors: an innovative approach to computerized histometry”, Eur. Urol. 35, 484–491 (1999). http://dx.doi.org/10.1159/000019884CrossrefGoogle Scholar

  • [13] T. Markiewicz, P. Wiśniewski, S. Osowski, J. Patera, W. Kozłowski, and R. Koktysz, “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). Google Scholar

  • [14] N. Zama and H. Katow, “A method of quantitative analysis of cell migration using a computerized time-lapse video-microscopy”, Zool. Sci. 5, 53–60 (1988). Google Scholar

  • [15] A. Korzynska, W. Strojny, A. Hoppe, D. Wertheim, and P. Hoser, “Segmentation of microscope images of living cells”, Pattern. Anal. Appl. 10, 301–319 (2007). http://dx.doi.org/10.1007/s10044-007-0069-7Web of ScienceCrossrefGoogle Scholar

  • [16] A. Boucher, A. Doisy, X. Ronot, and C. Garbay, “Cell migration analysis after in vitro wounding injury with a multi agent approach”, Artif. Intell. Rev. 12, 137–162 (1998). http://dx.doi.org/10.1023/A:1006500808998CrossrefGoogle Scholar

  • [17] L. Witkowski, “A computer system for cells motility evaluation”, Machine Graphics and Vision 17, 167–186 (2008). Google Scholar

  • [18] M. Iwanowski and A. Korzyńska, “Segmentation of moving cells in bright-field and epi-fluorescent microscopic image sequences”, in Lect. Notes Comput. Sc. 6374, pp. 401–410, edited by L. Bolc, R. Tadeusiewicz, L.J. Chmielewski, and K. Wojciechowski, Springer-Verlag, Berlin Heidelberg, 2010. Google Scholar

  • [19] D.L. Pham, C. Xu, and J.L. Prince, “A survey of current methods in medical image segmentation”, Annu. Rev. Biomed. Eng. 2, 315–338 (2000). http://dx.doi.org/10.1146/annurev.bioeng.2.1.315CrossrefGoogle Scholar

  • [20] D. Comaniciu and P. Meer, “Cell image segmentation for diagnostic pathology”, in Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Application in Cardiology, Neurology, Mammography and Pathology, pp. 541–558, edited by J.S. Suri, S.K. Setarehdan, Springer, 2001. Google Scholar

  • [21] H. Ramoser, “Leukocyte segmentation and SVM classification in blood smear images”, Machine Graphics and Vision 17, 187–200 (2008). Google Scholar

  • [22] S. Tse, L. Bradbury, J.W.L. Wan, H. Djambazian, R. Sladek, and T. Hudson, “A combined watershed and level set method for segmentation of bright field cell images”, Proc. SPIE 7258, 72593G–72593G-10 (2009). http://dx.doi.org/10.1117/12.811747Google Scholar

  • [23] M. Wang, X. Zhou, F. Li, J. Huckins, R. King, and S. Wong, “Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy”, Bioinformatics 24, 94–101 (2008). http://dx.doi.org/10.1093/bioinformatics/btm530CrossrefWeb of ScienceGoogle Scholar

  • [24] E. Alkuwari, K. Khetani, N. Dendukuri, L. Wang, and M. Auger, “Quantitative assessment of nuclear grooves in fine needle aspirates of the thyroid”, Anal. Quant. Cytol. Histol. 31, 161–169 (2009). Google Scholar

  • [25] A. Dulewicz, D. Piętka, and P. Jaszczak, “Digital image analysis in research and diagnosis of urinary bladder cancer”, in Bladder Cancer: Etymology, Diagnosis and Treatments, pp. 211–228, edited by W.E. Nilsson, Nova Science Biomedical Books, New York, 2010. Google Scholar

  • [26] T. Markiewcz and S. Osowski, “Morphological operations for blood cells extraction from the image of the bone marrow smear”, Prz. Elektrotechniczn. 84, 24–26 (2008). Google Scholar

  • [27] U. Neuman, A. Korzynska, C. Lopez, and M. Lejeun, “Segmentation of stained lymphoma tissue section images”, in Information Technology in Biomedicine 2, ASC 69, pp. 101–113, edited by E. Pieta and J. Kawa, Springer-Verlag, Berlin Heidelberg, 2010. Google Scholar

  • [28] T. Markiewicz, S. Osowski, J. Pater, and W. Kozlowski, “Image processing for accurate cell recognition and count on histologic slides”, Anal. Quant. Cytol. Histol. 28, 281–291 (2006). PubMedGoogle Scholar

  • [29] G. Kayser, D. Radziszowski, P. Bzdyl, R. Sommer, and K. Kayser, “Theory and implementation of an electronic, automated measurement system for images obtained from immunohistochemically stained slides”, Anal. Quant. Cytol. Histol. 28, 27–38 (2006). Google Scholar

  • [30] C. Lopez, M. Lejeune, M,T. Salvedo, P. Escriva, R. Bosh, L.E. Pons, T. Alvaro, J. Roig, X. Cugat, J. Baucells, and J. Jaen, “Automated quantification of immunohistochemical markers with different complexity”, Histochem. Cell Biol. 129, 379–287 (2008). http://dx.doi.org/10.1007/s00418-007-0368-5Web of ScienceCrossrefGoogle Scholar

  • [31] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification”, IEEE T. Syst. Man Cyb. 6, 610–621 (1973). http://dx.doi.org/10.1109/TSMC.1973.4309314CrossrefGoogle Scholar

  • [32] M. Iwanowski, Morphological Methods in Digital Image Processing, AOW EXIT, Warsaw, 2009. (in Polish) Google Scholar

  • [33] M. Nieniewski, Mathematical Morphology in Image Processing, PLJ, Warsaw, 1998. (in Polish) Google Scholar

  • [34] M. Iwanowski and J. Serra, “The morphological-affine object deformation”, Mathematical Morphology and its Applications to Signal and Image Processing, pp. 81–90, Kluwer Academic Publishers, 2000. Google Scholar

  • [35] J. Serra, Image Analysis and Mathematical Morphology, Academic Press 1, 1983. Google Scholar

  • [36] J. Serra, Image Analysis and Mathematical Morphology, Academic Press 2, 1988. Google Scholar

  • [37] P. Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag, 2004. Google Scholar

  • [38] N. Otsu, “A threshold selection method from grey level histograms”, IEEE T. Syst. Man Cyb. 9, 62–66 (1979). http://dx.doi.org/10.1109/TSMC.1979.4310076CrossrefGoogle Scholar

  • [39] L. Bużańska, E.K. Machaj, B. Zabłocka, Z. Podja, and K. Domańska-Janik, “Human cord blood-derived cells attain neuronal and glial features in vitro”, J. Cell Sci. 115, 2131–2138 (2002). Google Scholar

  • [40] M. Iwanowski and A. Korzyńska, “Detection of the area covered by neural stem cells in cultures using textural segmentation and morphological watershed”, Adv. Soft Comp. 3, pp. 543–557, edited by M. Kurzyński, E. Puchała, M. Woźniak, A. Żołnierek, Springer-Verlag, Berlin Heidelberg, 2009. Google Scholar

  • [41] http:\www.ibib.waw.pl/grants/korzynska Google Scholar

  • [42] A. Korzyńska and M. Iwanowski, “Artificial images for evaluation of segmentation results; bright field images of living cells”, accepted for LNCS, 2012. Google Scholar

  • [43] Y.J. Zhang and J.J. Gerbrands, “Objective and quantitative segmentation evaluation and comparison”, Signal Process 39, 43–54 (1994). http://dx.doi.org/10.1016/0165-1684(94)90122-8CrossrefGoogle Scholar

  • [44] A. Korzyńska, M. Iwanowski, U. Neuman, E. Dobrowolska, and P. Hoser, “Comparison of the methods of microscopic image segmentation”, IFMBE Proc. 25/IV, pp. 425–428, Munich, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_113CrossrefGoogle Scholar

About the article

Published Online: 2012-10-17

Published in Print: 2012-06-01

Citation Information: Opto-Electronics Review, Volume 20, Issue 2, Pages 174–186, ISSN (Online) 1896-3757, DOI: https://doi.org/10.2478/s11772-012-0026-x.

Export Citation

© 2012 SEP, Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in