An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

  • Nada Fitrieyatul Hikmah Institut Teknologi Sepuluh Nopember
  • Tri Arief Sardjono Department of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Windy Deftia Mertiana Department of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Nabila Puspita Firdi Department of Biomedical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Diana Purwitasari Department of Informatics Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Keywords: Breast Cancer, CC view, Entropy, Feature Extraction, Mammography, MLO view


Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.


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How to Cite
Nada Fitrieyatul Hikmah, Tri Arief Sardjono, Windy Deftia Mertiana, Nabila Puspita Firdi, & Diana Purwitasari. (2022). An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images. EMITTER International Journal of Engineering Technology, 10(1), 136-152.