Skip to main content

Advertisement

SpringerLink
Log in

P53immunostained cell nuclei segmentation in tissue images of oral squamous cell carcinoma

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper presents segmentation of p53immunostained tissue images of oral squamous cell carcinoma that consist of cell nuclei segmentation and splitting of overlapping/touching cell structures. In segmentation, the entropy thresholding has been adopted in which the optimum threshold value to each color component of the image is obtained by maximizing the global entropy computed from its gray-level co-occurrence matrix. The segmented image consists of isolated cells and complex nuclei structures. A novel complex nuclei structure detection algorithm is proposed to identify overlapped nuclei structures, which have been further resolved by watershed transform. The performance of the segmentation technique is evaluated using the quantitative metrics, namely mean absolute difference (MAD), dice coefficient (DC) and accuracy. Global entropy thresholding-based segmentation technique achieved the best MAD of 0.478, DC of 0.967 and accuracy of 0.970 compared to state-of-art techniques such as otsu and active contour. Extensive experimental results show that proposed complex nuclei structure detection-based overlapping/touching cells splitting algorithm effectively delineated nuclei with over- and under-segmentation rate of 0.49 %. Therefore, tissue image segmentation method presented has high potential in immunohistochemical (IHC) quantification and also can be easily generalized for images stained with other biomarkers.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Chang, S.W., Abdul-Kareem, S., Merican, A.F., Zain, R.B.: Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinform. 14(170), 1–15 (2013)

    Google Scholar 

  2. Polanska, H., Raudenska, M., Gumulec, J., Sztalmachova, M., Adam, V., Kizek, R., Masarik, M.: Clinical significance of head and neck squamous cell cancer biomarkers. Oral Oncol. 50(3), 168–177 (2014)

    Article  Google Scholar 

  3. Swaminathan, U., Joshua, E., Rao, U.K., Ranganathan, K.: Expression of p53 and cyclin D1 in oral squamous cell carcinoma and normal mucosa: an immunohistochemical study. J. Oral Maxillofac. Pathol (JOMFP) 16(2), 172 (2012)

    Article  Google Scholar 

  4. Ebrahimi, M., Boldrup, L., Coates, P.J., Wahlin, Y.B., Bourdon, J.C., Nylander, K.: Expression of novel p53 isoforms in oral lichen planus. Oral Oncol. 44(2), 156–161 (2008)

    Article  Google Scholar 

  5. Reibel, J.: Prognosis of oral pre-malignant lesions: significance of clinical, histopathological, and molecular biological characteristics. Crit. Rev. Oral Biol. Med. 14(1), 47–62 (2003)

    Article  Google Scholar 

  6. Di Cataldo, S., Ficarra, E., Macii, E.: Computer-aided techniques for chromogenic immunohistochemistry: status and directions. Comput. Biol. Med. 42(10), 1012–1025 (2012)

    Article  Google Scholar 

  7. Fuchs, T.J., Buhmann, J.M.: Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35(7), 515–530 (2011)

    Article  Google Scholar 

  8. Samsi, S., Krishnamurthy, A.K., Gurcan, M.N.: An efficient computational framework for the analysis of whole slide images: application to follicular lymphoma immunohistochemistry. J. Comput. Sci. 3(5), 269–279 (2012)

    Article  Google Scholar 

  9. Wienert, S., Heim, D., Kotani, M., Lindequist, B., Stenzinger, A., Ishii, M., Hufnagl, P., Beil, M., Dietel, M., Denkert, C., et al.: Cognitionmaster: an object-based image analysis framework. Diagn. Pathol. 8(1), 34–34 (2013)

    Article  Google Scholar 

  10. He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Comput. Methods Programs Biomed. 107(3), 538–556 (2012)

    Article  Google Scholar 

  11. Masmoudi, H., Hewitt, S.M., Petrick, N., Myers, K.J., Gavrielides, M.A.: Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans. Med. Imaging 28(6), 916–925 (2009)

    Article  Google Scholar 

  12. Di Cataldo, S., Ficarra, E., Acquaviva, A., Macii, E.: Automated segmentation of tissue images for computerized IHC analysis. Comput. Methods Programs Biomed. 100(1), 1–15 (2010)

    Article  Google Scholar 

  13. Chang, C.I., Chen, K., Wang, J., Althouse, M.L.: A relative entropy-based approach to image thresholding. Pattern Recognit. 27(9), 1275–1289 (1994)

    Article  Google Scholar 

  14. Chang, C.I., Du, Y., Wang, J., Guo, S.M., Thouin, P.: Survey and comparative analysis of entropy and relative entropy thresholding techniques. In: IEE Proceedings of the Vision, Image and Signal Processing, vol 153, pp. 837–850. IET (2006)

  15. Sertel, O., Lozanski, G., Shana’ah, A., Gurcan, M.N.: Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation. IEEE Trans. Biomed. Eng. 57(10), 2613–2616 (2010)

    Article  Google Scholar 

  16. Mudrova, M., Prochazka, A.: Principal component analysis in image processing. In: Proceedings of the MATLAB Technical Computing Conference, Prague (2005)

  17. Yimit, A., Hagihara, Y., Miyoshi, T., Hagihara, Y., Yimit, Q.: Fast method for two-dimensional renyi\(^{\prime }\)s entropy-based thresholding. Int. J. Comput. Sci. Eng. 4(2), 176–183 (2012)

    Google Scholar 

  18. Cloppet, F., Boucher, A.: Segmentation of complex nucleus configurations in biological images. Pattern Recognit. Lett. 31(8), 755–761 (2010)

    Article  Google Scholar 

  19. Mouelhi, A., Sayadi, M., Fnaiech, F., Mrad, K., Romdhane, K.B.: Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method. Biomed. Signal Process. Control 8(5), 421–436 (2013)

    Article  Google Scholar 

  20. Qi, X., Xing, F., Foran, D.J., Yang, L.: Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans. Biomed. Eng. 59(3), 754–765 (2012)

    Article  Google Scholar 

  21. 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 Regul. Pap. 53(11), 2405–2414 (2006)

    Article  Google Scholar 

  22. Veta, M., van Diest, P.J., Kornegoor, R., Huisman, A., Viergever, M.A., Pluim, J.P.: Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PLoS One 8(7), e70221 (2013)

    Article  Google Scholar 

  23. Lu, C., Mahmood, M., Jha, N., Mandal, M.: Detection of melanocytes in skin histopathological images using radial line scanning. Pattern Recognit. 46(2), 509–518 (2013)

    Article  Google Scholar 

  24. Zhang, L., Kong, H., Chin, C.T., Liu, S., Chen, Z., Wang, T., Chen, S.: Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts. Comput. Med. Imaging Graph. 38(5), 369–380 (2014)

    Article  Google Scholar 

  25. Di Cataldo, S., Ficarra, E., Acquaviva, A., Macii, E.: Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach: a quantitative evaluation. Comput. Med. Imaging Graph. 34(6), 453–461 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

The authors are expressing sincere thanks to Dr. Siow-Wee Chang, Faculty of Science, Institute of Biological Sciences, University of Malaya, for providing IHC-stained tissue images of OSCC used in this study. Special thanks to Dr. Anil Malleshi Betigeri, M.D., and Dr. Mathusudanan, M.D., Department of Pathology, Meenakashi Mission Hospital and Research Center, Madurai, Tamilnadu, India, for their supports in understanding the concepts and also for their valuable suggestions during this research work.

Author information

Authors and Affiliations

  1. Department of ECE, National College of Engineering, Tirunelveli, Tamilnadu, India

    K. A. Shahul Hameed

  2. Department of ECE, Thiagarajar College of Engineering, Madurai, Tamilnadu, India

    A. Banumathi

  3. Department of Oral Surgery, CSI College of Dental Science and Research, Madurai, Tamilnadu, India

    G. Ulaganathan

Authors
  1. K. A. Shahul Hameed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Banumathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Ulaganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. A. Shahul Hameed.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1606 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahul Hameed, K.A., Banumathi, A. & Ulaganathan, G. P53immunostained cell nuclei segmentation in tissue images of oral squamous cell carcinoma. SIViP 11, 363–370 (2017). https://doi.org/10.1007/s11760-016-0953-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0953-y

Keywords

Search

Navigation