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Automatic detection of breast cancer in mammogram images

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  • Faozia Ali S. Alsarori

    (Yildirim Beyazit University, Ankara, Turkey)

  • Reza Hassanpour

    (Çankaya University, Ankara, Turkey)

Abstract

The aim of this study is to harness the great potential of image processing techniques which have evolved signi?icantly in the last years, to build an automatic system to detect and diagnose breast cancer in the digital mammographic images in order to help those interested people in this domain, such as radiologists and specialists in oncology and to improve their performance by reducing error rates of breast cancer diagnosis. As long as segmentation and extracting the effective features of mammograms play a major role to isolate and classify suspicious regions which can be subject to cancer, in this work, we focus on abnormality detection using multi-thresholding OTSU's method to segment the Region Of Interest (ROI). Then the texture features of the segmented ROI are extracted which are used to classify the ROI as normal or abnormal tissue by using an Arti?icial Neural Network (ANN). This system can correctly classify the tested region by a rate of 93.80%.

Suggested Citation

  • 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.
    • Handle: RePEc:apb:jaterr:2016:p:196-201
    DOI: 10.20474/jater-2.6.4
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

    1. Cheng-Ho Chen & Yun-Sheng Ye & Wen-Tung Hsu, 2018. "Automatic venipuncture insertion point recognition based on machine vision," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 4(5), pages 186-190.

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