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The detection of cervical cancer disease using an adaptive thresholding method through digital image processing

Author

Listed:
  • Eggi I. Putri

    (Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia)

  • Rita Magdalena

    (Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia)

  • Ledya Novamizanti

    (Telkom Engineering School (TES)Telkom University (Tel-U), Bandung, Jawa Barat 40257, Indonesia)

Abstract

Cervical cancer is a kind of cancer disease caused by human papilloma virus type 16 and 18, attacking woman cervix. To detect a cervical cancer, the frequently-used method is Pap-Smear; however, errors often occur when the method is taken to diagnose the level of cervical cancer. Thus, a proper system is required, which is supposed for being able to help identifying the result of Pap-Smear. This study aims at designing a system to detect the symptoms of cervical cancer using MATLAB to solve these errors. The image processing begins with converting the type of an image, which is followed by a thresholding, and a noise removal using filters until the image has become ready to be detected. For a thresholding process, an Adaptive Thresholding method is taken, in which the thresholding focuses on local threshold values. The system is considerably able to classify images into two types, i.e. normal and abnormal (precancerous). Abnormal type is divided into three subtypes, i.e. mild, moderate and severe. An experiment is conducted on the proposed system, in which it is supposed to analyze 500 test images, including 250 for training and 250 for testing. Based on the testing process, a perfect 100% accuracy rate is obtained, while the average processing time is 25.4 seconds with a WS value at 10 and C value at -2.

Suggested Citation

  • Eggi I. Putri & Rita Magdalena & Ledya Novamizanti, 2015. "The detection of cervical cancer disease using an adaptive thresholding method through digital image processing," Journal of Advances in Health and Medical Sciences, Balachandar S. Sayapathi, vol. 1(1), pages 30-36.
    • Handle: RePEc:apb:jahmss:2015:p:30-36
    DOI: 10.20474/jahms-1.1.4
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    Cited by:

    1. Soraya Niha & Boonkanas Jantarasiriput & Narisara Tonyongdalaw & Navarat Vaichompu, 2016. "Reproductive Health Among Bangoebadae Muslim Women: Cervical Cancer Care," International Journal of Health and Medical Sciences, Mohammad A. H. Khan, vol. 2(3), pages 52-57.
    2. Faozia Ali S. Alsarori & Reza Hassanpour, 2016. "Automatic detection of breast cancer in mammogram images," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(6), pages 196-201.
    3. Nurjannah Syakrani & Rheza Ghivary Santoso, 2018. "Detection of melanoma cancer using gray level cooccurance matrix and artificial neural network methods," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(2), pages 91-101.

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