Abstract: In Maghreb countries, breast cancer considered one of the leading causes of mortality between females. A screening mammogram is a method of taking low energy level x-ray images of the human breast to identify the early symptoms of breast cancer. The shape and contour of the lesion in digitized mammograms are two effective features that allow the radiologists to distinguish between benign and malignant tumors. We propose in this paper a new approach based on Otsu’s thresholding method for semi-automatic extraction of lesion boundaries from mammogram images. This approach attempts to find the best threshold value where the weighted variance between the lesion and normal tissue pixels is the least. In the first step, the median filter is used for removing noise within the region of interest (ROI). In the second step, a global threshold decrement was started in order to get the proper range of pixels in which the breast lesion could be segmented by Otsu’s thresholding method with high accuracy. The technique of mathematical morphology, especially opening operation is used in this work for removing small objects from the ROI while preserving the shape and size of larger objects that represent the tumors. We evaluated our proposal using 21 images obtained from Mini-MIAS database. Experimental results show that our proposal achieves better results than many previously explored methods.

Moustapha Mohamed Saleck, M. H. Ould Mohamed Dyla, EL Benany Mohamed Mahmoud and Mohamed Rmili, “Otsu’s Thresholding for Semi-Automatic Segmentation of Breast Lesions in Digital Mammograms” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131044

@article{Saleck2022,
title = {Otsu’s Thresholding for Semi-Automatic Segmentation of Breast Lesions in Digital Mammograms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131044},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131044},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Moustapha Mohamed Saleck and M. H. Ould Mohamed Dyla and EL Benany Mohamed Mahmoud and Mohamed Rmili}
}