Abstract
Computer-assisted image analysis cytology play an important function in modern cancer diagnostics. A crucial task of such systems is segmentation of cell nuclei. Automatic procedure have to locate their exact position in cytological preparation and determine precise edges in order to extract morphometric features. Unfortunately, segmentation of individual nuclei is a huge challenge because they often creates complex clusters without clear edges. To deal with this problem we are proposing to combine Bayesian object recognition approach to approximate nuclei by circles with marker-controlled watershed employed to determine their exact shape. Watershed segmentation can reconstruct a precise shape of nuclei but only if their approximate location is known. On the other hand, Bayesian object recognition approach allows to isolate single nuclei even in complex nuclei structures but without determining their exact shape. Thus, we used Bayesian object recognition to generate markers required to form a topographic map for a watershed method. The effectiveness of the proposed approach was examined using artificially generated images and real cytological images of breast cancer. Tests carried out have shown that the proposed version of the marked-controlled watershed can be used with success to segment elliptic-shaped objects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baddeley, A.J., van Lieshout, M.N.M.: Stochastic geometry models in high-level vision. In: Mardia, K.V., Kanji, G.K. (eds.) Advances in Applied Statistics, Statistics and Images: 1, pp. 231–256. Carfax Publishing, Abingdon (1993)
Bembenik, R., Jóźwicki, W., Protaziuk, G.: Methods for mining co-location patterns with extended spatial objects. Int. J. Appl. Math. Comput. Sci. 27(4), 681–695 (2017)
Gdawiec, K.: Procedural generation of aesthetic patterns from dynamics and iteration processes. Int. J. Appl. Math. Comput. Sci. 27(4), 827–837 (2017)
Kłeczek, P., Dyduch, G., Jaworek-Korjakowska, J., Tadeusiewicz, R.: Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin. Proc. SPIE 10140, 10,140:1–10,140:19 (2017)
Kowal, M., Filipczuk, P.: Nuclei segmentation for computer-aided diagnosis of breast cancer. Int. J. Appl. Math. Comput. Sci. 24(1), 19–31 (2014)
Kowal, M., Korbicz, J.: Marked Point Process for Nuclei Detection in Breast Cancer Microscopic Images, pp. 230–241. Springer, Cham (2018)
van Lieshout, M.C.: A Bayesian approach to object recognition, pp. 185–190 (1991)
van Lieshout, M.N.M.: Markov point processes and their applications in high-level imaging. Bull. Int. Stat. Inst. 56, 559–576 (1995)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Paramanandam, M., O’Byrne, M., Ghosh, B., Mammen, J.J., Manipadam, M.T., Thamburaj, R., Pakrashi, V.: Automated segmentation of nuclei in breast cancer histopathology images. PLOS ONE 11(9), 1–15 (2016)
Pentland, A.: A method of measuring the angularity of sands. Proc. Trans. R. Soc. Can. 21(3), 43 (1927)
Piorkowski, A.: A statistical dominance algorithm for edge detection and segmentation of medical images. In: Information Technologies in Medicine. Advances in Intelligent Systems and Computing, vol. 471, pp. 3–14. Springer (2016)
Ritter, N., Cooper, J.: New resolution independent measures of circularity. J. Math. Imaging Vis. 35(2), 117–127 (2009)
Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)
Strauss, D.J.: A model for clustering. Biometrika 62(2), 467–475 (1975)
Veta, M., Huisman, A., Viergever, M.A., van Diest, P.J., Pluim, J.P.W.: Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 618–621 (2011)
Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)
Wiȩcławek, W., Piȩtka, E.: Watershed based intelligent scissors. Comput. Med. Imaging Graph. 43, 122–129 (2015)
Yang, X., Li, H., Zhou, X.: Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy. IEEE Trans. Circuits Syst. I Regular Papers 53(11), 2405–2414 (2006)
Acknowledgement
The research was supported by National Science Centre, Poland (2015/17/B/ST7/03704).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Skobel, M., Kowal, M., Korbicz, J., Obuchowicz, A. (2019). Cell Nuclei Segmentation Using Marker-Controlled Watershed and Bayesian Object Recognition. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-91211-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91210-3
Online ISBN: 978-3-319-91211-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)