Hyperchromatic Nucleus Segmentation on Breast Histopathological Images for Mitosis Detection


  • Tan Xiao Jian School of Mechatronic Engineering, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
  • Nazahah Mustafa School of Mechatronic Engineering, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
  • Mohd Yusoff Mashor School of Mechatronic Engineering, University Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
  • Khairul Shakir Ab Rahman Hospital Tuanku Fauziah, 01000 Kangar, Perlis, Malaysia.


Breast Cancer, Hyperchromatic Nucleus, Mitosis, Nucleus Candidates,


Breast cancer grading is the standard clinical practice for the prognosis and diagnosis of breast cancer development. The Nottingham Histological Grading (NHG) system is widely used in the breast cancer grading. In NHG system, the mitotic count based on histopathological images (i.e. microscopic slide examination) is one of the three criteria that define the overall grade. Image processing techniques such as segmentation could be utilised to detect mitotic cells. This study proposed a new approach to segment hyperchromatic nucleus on the histopathological images based on RGB and HSI colour spaces. The results show that the proposed segmentation technique could provide a promising result in segmenting hyperchromatic nucleus and preserving the ground truth (i.e. true mitotic cells).


Weigelt, B. and Reis-Filho, J. S., “Histological And Molecular Types Of Breast Cancer: Is There A Unifying Taxonomy,” Nat Rev Clin Oncol, 6, 718-730. 10.1038/nrclinonc.2009.166, 2009.

GBD Compare. [online] http://vizhub.healthdata.org/gbd-compare/ (Accessed 12 December 2016).

H. J. G. B. & W.W.Richardson, “Histological grading and prognosis of breast cancer,” vol. 22, no. 1, pp. 36–37, 1957.

O. Sertel, U. V. Catalyurek, H. Shimada, and M. N. Gurcan, “Computer-aided prognosis of neuroblastoma: Detection of mitosis and karyorrhexis cells in digitized histological images,” Proc. 31st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Eng. Futur. Biomed. EMBC 2009, pp. 1433–1436, 2009.

M. Veta, P. J. van Diest, R. Kornegoor, A. Huisman, M. A. Viergever, and J. P. W. Pluim, “Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images,” PLoS One, vol. 8, no. 7, pp. 1– 12, 2013.

E. a Rakha, J. S. Reis-Filho, F. Baehner, D. J. Dabbs, T. Decker, V. Eusebi, S. B. Fox, S. Ichihara, J. Jacquemier, S. R. Lakhani, J. Palacios, A. L. Richardson, S. J. Schnitt, F. C. Schmitt, P.-H. Tan, G. M. Tse, S. Badve, and I. O. Ellis, “Breast cancer prognostic classification in the molecular era: the role of histological grade.,” Breast Cancer Res., vol. 12, no. 4, p. 207, 2010.

L. Pantanowitz, N. Farahani, and A. Parwani, “Whole slide imaging in pathology: advantages, limitations, and emerging perspectives,” Pathol. Lab. Med. Int., vol. 7, p. 23, 2015.

X. Yang, H. Li, and X. Zhou, “Nuclei Segmentation Using MarkerControlled Watershed , Tracking Using Mean-Shift , and Kalman Filter in Time-Lapse Microscopy,” IEEE Trans. Circuits Syst., vol. 53, no. 11, pp. 2405–2414, 2006.

K. Lee, W. Street, and K.-M. Lee, “A fast and robust approach for automated segmentation of breast cancer nuclei,” Proc. IASTED Int., 1999.

Nedzved, S. Ablameyko, and I. Pitas, “Morphological segmentation of histology cell images,” Proc. 15th Int. Conf. Pattern Recognit., vol. 1, pp. 500–503, 2000.

Pourakpour F. and Ghassemian H., “Automated Mitosis Detection Based on Combination of Effective Textural and Morphological Features from Breast Cancer Histology Slide Images,” no. November, pp. 25–27, 2015.

A. M. Khan, H. Eldaly, and N. M. Rajpoot, “A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images.,” J. Pathol. Inform., vol. 4, no. Icpr, p. 11, 2013.

V. M., P. J.P.W., V. D. P.J., and V. M.A., Breast cancer histopathology image analysis: A review, vol. 61, no. 5. 2014.

M. Veta, P. J. Van Diest, S. Willems, H. Wang, A. Madabhushi, F. Gonzalez, A. a C. Roa, A. B. L. Larsen, J. S. Vestergaard, B. Dahl, D. C. Cireșan, J. Schmidhuber, A. Giusti, and M. Luca, “Assessment of mitosis detection algorithms in breast cancer histopathology images,” Med. Image Anal., vol. 2013, pp. 1–21, 2013.

M. Zhao, C. Zhang, W. Zhang, W. Li, and J. Zhang, “Decorrelationstretch based cloud detection for total sky images,” 2015 Vis. Commun. Image Process. VCIP 2015, pp. 0–3, 2016.

N. Otsu, “A threshold selection method from gray level histograms.,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.

E. Cosatto, M. Miller, H. P. Graf, and J. S. Meyer, “Grading nuclear pleomorphism on histological micrographs,” Pattern Recognition, 2008. ICPR 2008. 19th Int. Conf., no. August 2016, pp. 1–4, 2008.




How to Cite

Jian, T. X., Mustafa, N., Mashor, M. Y., & Ab Rahman, K. S. (2018). Hyperchromatic Nucleus Segmentation on Breast Histopathological Images for Mitosis Detection. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 27–30. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4070