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A Colour Space Based Detection for Cervical Cancer Using Fuzzy C-Means Clustering

Published: 22 June 2017 Publication History

Abstract

This research presents a colour segmentation method using Hue, Saturation, Value (HSV) colour space based on fuzzy c-means clustering (FCM) to segment nucleus from single cell Pap smear images. Nucleus is a structural part of cell which can indicate whether a cell is normal or abnormal. This research aims to analyze the performance of colour space in the segmentation process. Pap smear images were segmented in HSV colour space by using fuzzy c-means clustering technique. Compared with segmentation process directly on HSV channel, the segmentation of each channel in space H, S and V were proposed. The segmentation results on each channel that has been applied roundness detection subsequently merged as the final segmentation and labeled as a nucleus. This research used 70 single cell Pap smear images taken in harlev dataset to examine the proposed segmentation method. The calculation of segmentation performance used the measurement based on precision, recall, and Zijdenbox Similarity Index (ZSI). The result showed that the proposed method generated precision, recall, and ZSI by 93%, 94%, and 93%.

References

[1]
WHO, 2016, WHO | Human Papillomavirus (HPV) and cervical cancer.[Online].Available:http://www.who.int/mediacentre/factsheets/fs380/en/. [Accessed: 04-Sep-2016].
[2]
Health Information [Online]. Available: www.depkes.go.id/resources/download/pusdatin/infodatin/infodatin-kanker.pdf [Accessed: 03-Mar-2017].
[3]
R. J. Kurman, D. E. Henson, A. L. Herbst, K. L. Noller, and M. H. Schiffman, Interim guidelines for management of abnormal cervical cytology. The 1992 National Cancer Institute Workshop, JAMA, vol. 271, no. 23, pp. 1866--1869, Jun. 1994.
[4]
K. Agarwal, Instruments and procedures in obstetrics and gynecology. JP Medical Ltd, 2014.
[5]
T. Guan, D. Zhou, W. Fan, K. Peng, C. Xu, and X. Cai, Nuclei enhancement and segmentation in color cervical smear images, 2014, pp. 107--112.
[6]
P. Sobrevilla, E. Montseny, F. Vaschetto, and E. Lerma, Fuzzy-based analysis of microscopic color cervical pap smear images: nuclei detection, Int. J. Comput. Intell. Appl., vol. 09, no. 03, pp. 187--206, Sep. 2010.
[7]
T. Chankong, N. Theera-Umpon, and S. Auephanwiriyakul, Automatic cervical cell segmentation and classification in pap smears, Comput. Methods Programs Biomed., vol. 113, no. 2, pp. 539--556, Feb. 2014.
[8]
J. R. Tang, N. A. M. Isa, and E. S. Ch'ng, Segmentation of cervical cell nucleus using intersecting cortical model optimized by particle swarm optimization, in Control System, Computing and Engineering (ICCSCE), 2015 IEEE International Conference on, 2015, pp. 111--116.
[9]
D. J. Bora and A. K. Gupta, A new approach towards clustering based color image segmentation, Int. J. Comput. Appl., vol. 107, no. 12, 2014.
[10]
J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard, Pap-smear benchmark data for pattern classification, Nat. Inspired Smart Inf. Syst. NiSIS 2005, pp. 1--9, 2005.
[11]
Shunyong Zhou, Wenling Xie, Cuixia Guo, and Bo Hu, A modified color image segmentation method based on fcm and region merging, 2011, pp. 3810--3813.
[12]
K.-S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, and T.-J. Chen, Fuzzy c-means clustering with spatial information for image segmentation,Comput. Med. Imaging Graph., vol. 30, no. 1, pp. 9--15, Jan. 2006.
[13]
A. Gençtav, S. Aksoy, and S. Önder, Unsupervised segmentation and classification of cervical cell images,Pattern Recognit., vol. 45, no. 12, pp. 4151--4168, 2012.

Cited By

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  • (2022)An Improved Deep Learning Based Cervical Cancer Detection Using a Median Filter Based PreprocessingEuropean Journal of Science and Technology10.31590/ejosat.1045538Online publication date: 1-Jan-2022
  • (2021)Cytology Image Analysis Techniques Toward AutomationACM Computing Surveys10.1145/344723854:3(1-41)Online publication date: Jun-2021

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  1. A Colour Space Based Detection for Cervical Cancer Using Fuzzy C-Means Clustering

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    ICBBS '17: Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science
    June 2017
    184 pages
    ISBN:9781450352222
    DOI:10.1145/3121138
    © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    • Natl University of Singapore: National University of Singapore

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    Published: 22 June 2017

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    Author Tags

    1. Colour segmentation
    2. FCM
    3. HSV colour space
    4. Pap smear images
    5. nuclei segmentation

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    • (2022)An Improved Deep Learning Based Cervical Cancer Detection Using a Median Filter Based PreprocessingEuropean Journal of Science and Technology10.31590/ejosat.1045538Online publication date: 1-Jan-2022
    • (2021)Cytology Image Analysis Techniques Toward AutomationACM Computing Surveys10.1145/344723854:3(1-41)Online publication date: Jun-2021

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