Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

Authors

  • Rahadian Kurniawan Universitas Islam Indonesia
  • Izzati Muhimmah Universitas Islam Indonesia
  • Arrie Kurniawardhani Universitas Islam Indonesia
  • Indrayanti Indrayanti Universitas Muhammadiyah Yogyakarta

DOI:

https://doi.org/10.21512/commit.v11i1.1957

Keywords:

Cervical Cancer, Overlapping Cells, Dis-tance Metrics, Morphological Operation

Abstract

The automatic interpretation of Pap Smear image is one of challenging issues in some aspects. Accurate segmentation for each cell is an important procedure
that must be done so that no information is lost during the evaluation process. However, the presence of overlapping cells in Pap Smear image make the automated analysis of these cytology images become more difficult. In most of
the studies, cytoplasm segmentation is the difficult stage because the boundaries between cells are very thin. In this study, we propose an algorithm that can segment the overlapping cytoplasm. First, the morphology operation and global thresholding to segment cytoplasm is done. Second, the overlapping area on cytoplasm region is separated using morphological operation and distance criteria on each pixel. The proposed method has been evaluated against the results of manual tracing by experts. The experiment results show that the proposed method can segment the overlapping cytoplasm as similar as experts do, i.e., 2:897 3:632 (mean std) using Hausdorff distance.

Dimensions

Plum Analytics

Author Biographies

Rahadian Kurniawan, Universitas Islam Indonesia

Department of Informatics, Industrial Technology Faculty

Izzati Muhimmah, Universitas Islam Indonesia

Department of Informatics, Industrial Technology Faculty

Arrie Kurniawardhani, Universitas Islam Indonesia

Department of Informatics, Industrial Technology Faculty

Indrayanti Indrayanti, Universitas Muhammadiyah Yogyakarta

Medical Faculty

References

WHO. (2012) Cervical cancer estimated incidence, mortality and prevalence worldwide in 2012. Accessed: 26-Jan-2016. [Online]. Available: http://globocan.iarc.fr/old/FactSheets/ cancers/cervix-new.asp

M. E. Plissiti, C. Nikou, and A. Charchanti, “Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering,” IEEE Transactions on information technology in biomedicine, vol. 15, no. 2, pp. 233–241, 2011.

I. Muhimmah, R. Kurniawan, and Indrayanti, “Automatic epithelial cells detection of pap smears images using fuzzy c-means clustering,” in 4th International Conference on Bioinformat-ics and Biomedical Technology, 2012, pp. 122– 127.

A. Garrido and N. P. De La Blanca, “Applying de-formable templates for cell image segmentation,” Pattern Recognition, vol. 33, no. 5, pp. 821–832, 2000.

E. Bak, K. Najarian, and J. P. Brockway, “Effi-cient segmentation framework of cell images in noise environments,” in Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th An-nual International Conference of the IEEE, vol. 1. IEEE, 2004, pp. 1802–1805.

I. Muhimmah, R. Kurniawan et al., “Automated cervical cell nuclei segmentation using morpho-logical operation and watershed transformation,” in Computational Intelligence and Cybernetics (CyberneticsCom), 2012 IEEE International Con-ference on. IEEE, 2012, pp. 163–167.

S.-F. Yang-Mao, Y.-K. Chan, and Y.-P. Chu, “Edge enhancement nucleus and cytoplast con-tour detector of cervical smear images,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 2, pp. 353–366, 2008.

K. Li, Z. Lu, W. Liu, and J. Yin, “Cytoplasm and nucleus segmentation in cervical smear images using radiating gvf snake,” Pattern Recognition, vol. 45, no. 4, pp. 1255–1264, 2012.

J. Jantzen, J. Norup, G. Dounias, and B. Bjer-regaard, “Pap-smear benchmark data for pattern classification,” in Proceedings of Nature inspired Smart Information Systems (NiSIS), 2005, pp. 1– 9.

N. Mustafa, N. A. M. Isa, and M. Y. Mashor, “Automated multicells segmentation of thinprep image using modified seed based region grow-ing algorithm (special issue on biosensors: Data acquisition, processing and control),” Biomedical fuzzy and human sciences: the official journal of the Biomedical Fuzzy Systems Association, vol. 14, no. 2, pp. 41–47, 2009.

R. S. Hoda and S. A. Hoda, Fundamentals of Pap test cytology. Springer Science & Business Media, 2007.

I. Muhimmah, R. Kurniawan et al., “Analysis of features to distinguish epithelial cells and inflammatory cells in pap smear images,” in The 6th Internasional Conference on Biomedical En-gineering and Informatics, 2013, pp. 519–523.

T. Ridler, S. Calvard et al., “Picture thresholding using an iterative selection method,” IEEE trans syst Man Cybern, vol. 8, no. 8, pp. 630–632, 1978.

R. Kurniawan, “Modified watershed algorithm based on distance-metric criterion for nuclei clus-tered separation in pap smear images,” Jurnal Teknoin, vol. 19, no. 1, 2016.

R. S. Gonzalez and P. Wintz, Digital Image Pro-cessing. Addision-Wesley Publishing Compan, 1977.

M. E. Plissiti, M. Vrigkas, and C. Nikou, “Seg-mentation of cell clusters in pap smear images using intensity variation between superpixels,” in Systems, Signals and Image Processing (IWSSIP), 2015 International Conference on. IEEE, 2015, pp. 184–187.

D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, “Comparing images using the haus-dorff distance,” IEEE Transactions on pattern analysis and machine intelligence, vol. 15, no. 9, pp. 850–863, 1993.

X. Yang, H. Li, and X. Zhou, “Nuclei segmenta-tion using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 53, no. 11, pp. 2405–2414, 2006.

D. M. Ushizima, A. G. Bianchi, and C. M. Carneiro, “Segmentation of subcellular compart-ments combining superpixel representation with voronoi diagrams,” Overlapping Cervical Cytol-ogy Image Segmentation Challenge-IEEE ISBI, pp. 1–2, 2014.

S. N. A. M. Kanafiah, Y. Jusman, N. A. M. Isa, and Z. Mohamed, “Radial-based cell for-mation algorithm for separation of overlapping cells in medical microscopic images,” Procedia Computer Science, vol. 59, pp. 123–132, 2015.

Downloads

Published

2017-08-01
Abstract 807  .
PDF downloaded 534  .