Using Morphological Operation and Watershed Techniques for Breast Cancer Detection

Authors

  • Sarah Faris Ameer
  • Zinah Tareq Nayyef
  • Zena Hussain Fahad
  • Ibtihal Razaq Niama ALRubee

DOI:

https://doi.org/10.3991/ijoe.v16i05.12999

Keywords:

Breast cancer, Mammogram images, Morphological operation, Watershed transforms, Image Processing

Abstract


Breast cancer is one of the leading causes of mortality between women, with one in eight women diagnosed with the disease, but early detection can reduce death rates. Therefore, continuous effort is being made to advance more effective methods for the early and effective diagnosis of breast cancer with high accuracy without human intervention. Classical attempts were manual, time- consuming and ineffective in many situations. The purpose of this work is to detect and locate the presence of malignant tissues in the breast using the morphological technique in mammogram images to diagnose breast cancer because morphology is one of the most reliable methods for early detection of breast cancer. The proposed algorithm is developed using watershed segmentation after the preprocessing is completed by the median filter to eliminate any expected noise, and contouring the tumor by morphological techniques to take the best diagnostic for breast cancer in a mammogram image. Good results are obtained for the measurements used like MSE, PSNR, SNR, entropy for the mammogram images.   

Author Biographies

Sarah Faris Ameer

Department of Computer science, Dijlah University College,

Ministry of Justice, Notary public office

Zinah Tareq Nayyef

Department of Computer science, Dijlah University College

Zena Hussain Fahad

Department of Computer science, Dijlah University College

Ibtihal Razaq Niama ALRubee

Electrical Engineering Department

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Published

2020-05-14

How to Cite

Ameer, S. F., Nayyef, Z. T., Fahad, Z. H., & Niama ALRubee, I. R. (2020). Using Morphological Operation and Watershed Techniques for Breast Cancer Detection. International Journal of Online and Biomedical Engineering (iJOE), 16(05), pp. 140–149. https://doi.org/10.3991/ijoe.v16i05.12999

Issue

Vol. 16 No. 05 (2020)

Section

Short Papers

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