Skip to main content
Log in

Segmentation of microscope images of living cells

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

This paper describes a segmentation method combining a texture based technique with a contour based method. The technique is designed to enable the study of cell behaviour over time by segmenting brightfield microscope image sequences. The technique was tested on artificial images, based on images of living cells and on real sequences acquired from microscope observations of neutrophils and lymphocytes as well as on a sequence of MRI images. The results of the segmentation are compared with the results of the watershed and snake segmentation methods. The results show that the method is both effective and practical.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bary D (2002) Cell movement. Garland, New York

    Google Scholar 

  2. Pollack Gerald H (2001) Cells, gels and the engines of life, 2001. Ebner & Sons, Seattle

    Google Scholar 

  3. Soll DR, Wessels D (1998) Motion analysis of living cells. Wiley-Liss, New York

    Google Scholar 

  4. Stossel TP (1993) On the crawling of animal cells. Science 260:1086–1094

    Article  Google Scholar 

  5. Abercrombie M, Heaysman JEM (1953) Observation on the social behaviour of cells in tissue culture. Speed of movement of chick heart fibroblasts in relation to their mutual contacts. Exp Cell Res 5:111–131

    Article  Google Scholar 

  6. Doroszewski J, Nowak-Wyrzykowska M, Stoowska L (1993) The method of moments as applied to the study of granulocytes’ shape and movement. Mater Med Polonia 2:87–92

    Google Scholar 

  7. Kowalczynska HM, Nowak-Wyrzykowska M, Kolos R, Dobkowski J, Kaminski J (2005) Fibronectin adsorption and arrangement on copolymer surfaces and their significance in cell adhesion. J Biomed Mater Res A 72(2):228–36

    Google Scholar 

  8. Litniewaski J, Bereiter-Hahn J (1990) Measurment of cells in culture by scanning acustic microscopy. J Microsc 158:95–107

    Google Scholar 

  9. Madeja Z, Kupiec M, Korohoda W (1998) Morphometric analysis of LPS-stimulated humane monocytes computer-assisted image analysis. Folia Biol 46(3–4):123–128

    Google Scholar 

  10. Soll DR (1995) The use of computer in understanding how animal cells crawl. Int Rev Cytol 163:43–104

    Article  Google Scholar 

  11. Warchol W, Warchol JB, Filipiak K, Karas Z, Jaroszyk F (1996) Analysis of spermatozoa movement using a video imaging technique. Histochem bell Biol 10:521–526

    Google Scholar 

  12. Zylicz M, Bocian K, Korczak-Kowalska G (2005) Regulatory cells: their development, mechanisms and effects of action, and their potential use in transplantation. Postepy Hig Med Dosw (Online) 59:160–71

    Google Scholar 

  13. Zicha D, Dann G (1995) An image processing system for cell behaviour studies in subconfluent cultures. J Microsc 179:11–21

    Google Scholar 

  14. Miroslaw L, Chorazyczewski A, Buchholz F, Kittler R (2005) Correlation-based method for automatic mitotic cell detection in phase contrast microscopy. In: Kurzynski M, Puchala E, Wozniak M, Zolnierek A (eds) Computer recognition systems. Springer, Berlin, pp 627–634

    Google Scholar 

  15. Smereka M (2005) Detection of ellipsoidal shapes using contour grouping. In: Kurzynski M, Puchala E, Wozniak M, Zolnierek A (eds) Computer recognition systems. Springer, Berlin, pp 443–450

    Google Scholar 

  16. Killich T, Plath PJ, WeiX, Bultmann H, Rensing L, Vicker MG (1993) The locomotion, shape and pseudopodial dynamics of unstimulated dictostelium cell are not random. J Cell Sci 106:1005–1013

    Google Scholar 

  17. Francis K, Ramakishna R, Holloway W, Palsson BO (1998) Two new pseudopod morphologies displayed by the human hematopoietic KG1a progenitor cell line and by primary human CD34+ cells. Blood 92(10):3616–3623

    Google Scholar 

  18. Curtis ASG (1998) Cell activation and adhesion. J Cell Sci 87:609–611

    Google Scholar 

  19. Lazarides E, Revel JP (1979) The molecular basis of cell movement. Sci Am 8:88–100

    Google Scholar 

  20. Lackie JM (1986) Cell movement ind cell behaviour. Allen & Unwin, London

    Google Scholar 

  21. Walmod OS, Hartman-Petersen R, Berezin A, Prag S, Kiselyov VV, Berezin V, Bock E (2001) Evaluation of individual-cell motility. Methods Mol Biol 161:59–83

    Google Scholar 

  22. Zama N, Katow H (1988) A method of quantitative analysis of cell migration using a computerized time-lapse videomicroscopy. Zool Sci 5:53–60

    Google Scholar 

  23. Soll DR, Voss E (1998) Two- and three-dimensional computer system for analysing how animal cells crawl. In: Soll DR, Wessels D (eds) Motion analysis of living cells. Wieley-liss, New York, pp 25–52

    Google Scholar 

  24. Bhargava MM, Li Y, Joseph A, Jin L, Rosen EM, Goldberg ID (1993) HGF-SF: effects on motility and morphlogy of normal and tumor cells. In: Golberg ID, Rosen EM (eds) Hepatocyte growth FACTOR-scatter fector and C-met receptor. Birkhausser Verlag, Basel, pp 341–349

    Google Scholar 

  25. Thuston G, Spadinger I, Palcic B (1991) Computer automation inmeasurment and analysis of cell motility in vitro. In: Goldberg ID (eds) Cell motility factors. Birkhausser Verlag, Basel, pp 206–222

    Google Scholar 

  26. Hoppe A, Wertheim D, Jiang WG, Williams R, Harding K (1999) Interactive image processing system for assessment of cell movement. Med Biol Eng Comput 37(4):419–423

    Article  Google Scholar 

  27. Korzynska A (2001) Computer aided neutrophil granulocytes movement and shape assessment. Ph.D. thesis, Prace Instytutu Biocybernetyki i Inzynierii Biomedycznej, 57, Warsaw (Polish)

  28. Korzynska A (1993) A method of cells’ movement investigation. In: Kulikowski JL (ed) Selected topics in biomedical image processing. Polish Academy of Sciences, Warsaw

    Google Scholar 

  29. Korzynska A, Nechay A, Bernatowska-Matuszkiewicz E, Skopczynska H (1996) Comparison of chosen movement parameters of neutrophils in healthy children and the Chediak–Higashi’s syndrome patient; signal investigation. In: Abstrakty III Konferncji Naukowej: Wybrane Zagadnienia z Immunologii Klinicznej, Warsaw, pp 18

  30. Korzynska A (1996) Analysis of some parameters in selected phases of cell’s motility: an image of normal and disordered crawling movement of human neutrophils in computer aided video enhanced microscopy. In: Proceedings of Cytokinematics’96

  31. Korzyska A, Nechay A, Mazur P, Pietka D, Kowal M (1997) Computer aided microscopy system on investigation of cell’s motility. In: Book of Abstract of 4th European Conference on Engineering and Medicine, Warszawa, pp 360–361

  32. Korzynska A, Hoppe A, Strojny W, Wertheim D (2003) Segmentation of neutrophil images using texture analysis and a contour based technique. In: Abstract book of KOSYR2003, Wroclaw, pp 29–33

  33. Russ JC (1995) Image processing handbook, 2nd edn. CRC Press, Boca Raton, Ann Arbor, London, Tokyo

    Google Scholar 

  34. Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD (1994) Molecular biology of the cell, 3rd edn. Garland Publishing Inc., New York & London

    Google Scholar 

  35. Fu KS, Mui JK (1981) A survey on image segmentation. Pattern Recognit 13:3–16

    Article  MathSciNet  Google Scholar 

  36. Pham DL, Chenyang X, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337

    Article  Google Scholar 

  37. Haralik RM, Shanmugan K, Dinstein I (1793) Textual Features for image Clssification. IEEE Trans Syst Man Cybern 3(6):610–621

    Google Scholar 

  38. Prewitt JMS (1970) Object enhancement and extraction. In: Lipkin BS, Rosenfeld A (eds) Picture processing and psychopictorics. Academic, New York, pp 75–149

    Google Scholar 

  39. Korzynska A (2002) Neutrophils’ movement in vitro. Ann NY Acad Sci 972:139–143

    Article  Google Scholar 

  40. Ahlberg JH, Nilson EN, Wash JL (1967) The theory of splines and their applications. Academic, New York

    MATH  Google Scholar 

  41. Nguyen HT, Worring M, van den Boomgaard R (2003) Watersnakes: energy-driven watershed segmentation. IEEE Trans Pattern Anal Mach Intell 25(3):330–342

    Article  Google Scholar 

  42. Jung CR, Scharcanski J (2005) Robust watershed segmentation using wavelets. Image Vis Comput 23:661–669

    Article  Google Scholar 

  43. Gonzalez Rafael C, Woods Richard E (2001) Digtal image processing. Prentice-Hall, New Jersy

    Google Scholar 

  44. Bovik A (ed) (2000) Handbook of video & image processing. Academic, London

  45. Tang J, Acton ST (2004) Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes. IEEE Trans Biomed Eng 51(2):316–324

    Article  Google Scholar 

  46. Kass M, Witkin A, Terzopolous D (1987) Snakes: active contour models. Int J Comput Vis 1:321–331

    Article  Google Scholar 

  47. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgement

We are grateful for the support received from the British Council in the form of the travel grant no. WAR 341/235, which allowed us to exchange ideas and jointly develop the combined method.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna Korzynska.

Appendix

Appendix

To determine the intersection of the cell’s contour with the line of the profile, the point of the highest curvature of the graph of the Cumulated normalized Prewitt gradient operator Function (called CPF) is calculated. The point of the highest curvature is called the bending point. This is done in three steps, first, the CPF is smoothened F applying a polynomial approximation of discretized Gaussian convolution:

$$ F\left(i\right)=\sum^{RmW}_{j=-RmW}w\left(j/RmW\right){\rm CPF}\left(i+j\right) $$
(7)

where w(j/RmW) are the weight coefficients of the smoothening operator, and RmW is its width.

Let r be parametrization of the curve:

$$ r:\left[0,T\right]\rightarrow R^2 $$
(8)

or of the broken line (Fig. 10):

$$ r:N\rightarrow R^2 $$
(9)

then

$$ r(t)\in R^2;\quad r(t)=\left[r_x(t), r_y(t)\right] $$
(10)
Fig. 10
figure 10

The approximation of the curvature for broken line

In the second step, the curvature of the smoothened CPF graph is calculated. For a continuous and differentiable curve, the value of the curvature is defined to be the inverse of the radius of the second order tangent circle at a given point (Fig. 10). Unfortunately, this definition fails in our case, since the cumulated gradient function is a non-differentiable broken line. A convenient approximate expression for curvature has, therefore, to be derived:

$$ K^*\left(t,H\right)=4{\frac{x_ny_p-x_py_n+x_py_s-x_sy_p+x_sy_n-x_ny_s} {(x^2_p+y^2_p+x^2_n+y^2_n-2x_px_n-2y_py_n)^{3/2}}} $$
(11)

where

  • x p  = r x (tH)  x s  = r x (t)  x n  = r x (t + H)

  • y p  = r y (tH)  y s  = r y (t)  y n  = r y (t + H)

  • H width of curvature operator (in t parameter space)

  • r the parameterisation of curve in plane (function)

In our case, (9) is applied to the graph. This amounts to substituting r x (t) = t, and r y (t) = F (t) in (9). The fact that a discrete set of the values is given in equally spaced points also has to be taken into account. Therefore, H is regarded as the distance between two consecutive points in the curvature operator.

It should be noted that operator K * may also be used for numerical calculation of the curvature of a smooth curve on a plane: it can be shown that if r(t) is a curve of the class C 2 then

$$ \lim_{H\rightarrow 0}K^*\left(t,H\right)={\frac{\dot{x}\ddot{y}- \ddot{x}\dot{y}}{(\dot{x}^2+\dot{y}^2)^{3/2}}}(t)=K(t) $$
(12)

Finally, in the last step, the point of a certain curvature (e.g. maximum curvature point), hence the intersection of the cell’s contour with the profile line is obtained based on the bending operator analysis. Because of the width of the operator arm, the H parameter, the maximum of the bending operator is slightly moved to the last almost vertical path of the s-shaped typical profile line (Fig. 11a), so the strategy for the finding that point is to determine the t for which the bending operator value reaches the value of the standard deviation of the bending operator calculated for all points along the part of the line profile. The part is chosen from the point distanced of H from 0 to the point distanced of H from the end of the profile line, it means for discrete points set H, H + 1, …, LH. If there are several such points, the strategy of one point determination is following: t is the first nearest point of the t max point, while t max is the point value of K * attains its maximum. The first is determined according to the order of analysis which starts from t max and goes along profile lines in the cell’s central point direction.

Fig. 11
figure 11

Curvature analysis of the cumulated normalized Prewitt gradient function (CPF): a 36 functions on the combined graph; these are the cumulated normalized Prewitt gradient function of pixels along 36 profile lines placed radially from the cell center point from 10° angle distance on cell 1 shown in Figs. 1 and 2 and b the s-shaped function, indicated by a dashed line, with the bending operator function of the pixels along a profile line, indicated by a continuous line, and with the straight vertical line, which shows the value of the bending operator standard deviation, indicated by dotted line, c the increasing function with the bending operator function and with the straight vertical line showing the value of the bending operator standard deviation—all indications similar to subimage b indicators. The stars on the profile lines show the position of the edge found according to the strategy described in the text

In Fig. 11a, there are the cumulated normalized Prewitt gradient function of pixels along 36 profile lines placed radially from cell’s center point (see some of them in Fig. 2f) in 10° angle distance on the cell 1 shown in Figs. 1 and 2. Most of these functions are s-shaped but there are a few in the form of the raising wavy line. Both types of functions are shown in Fig. 11 respectively in path b and c (dashed line). The s-shaped function is typical of a well defined cell border part (b) while the raising wavy function is typical of barely visible or not visible cell contour (c). Both types of functions are shown with the bending operator function of the pixels along the profile line (continuous line) and with straight vertical lines which show the value of the bending operator standard deviation (dotted line). The boundary points are marked as stars in both Fig. 11b and c.

The values of the RmW and H parameters according to the length profile lines L = 160 pixels have been examined. To do this a special tool has been developed in MATLAB. The interface of the tool allows adjusting the values of both parameters with a bar under observation of the position of the cell’s edge for a chosen line or for all profile lines. In the case of moving neutrophil image sequences, the 30 pixels distance of the H arm and the 10 pixels distance of the width of the smoothing operator RmW are chosen experimentally for the length of the profile lines L = 160 pixels. According to the analysis in Sect. 15 for small rounded cells, all the parameters should be shorten proportionally, e.g. in the case of the smallest artificial object (Fig. 8) the following values were used: L = 100, RmW = 5 and H = 16.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Korzynska, A., Strojny, W., Hoppe, A. et al. Segmentation of microscope images of living cells. Pattern Anal Applic 10, 301–319 (2007). https://doi.org/10.1007/s10044-007-0069-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-007-0069-7

Keywords

Navigation