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.


Download data is not yet available.


H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021.

W. A. Berg and J. W. T. Leung, Diagnostic Imaging Breast, Elsevier, Ed. 3, pp. 4-6, 2019.

L. W. Bassett, M. C. Mahoney, S.K. Apple, and C. J. D’Orsi, Breast Imaging, Saunders Elsevier (Philadelphia), Ed. 1, pp. 25-45, 2011.

A. B. R. Arafah and H. B. Notobroto, Factors Related to the Behavior of Housewives Performing Breast Self-Examination, The Indonesian Journal of Public Health, vol. 12, pp. 143-153, 2017.

A. S. A. B. Sama and S. M. S. Baneamoon, Breast Cancer Classification Enhancement Based on Entropy Method, International Journal of Engineering and Applies Computer Science, vol. 2, pp. 267-271, 2017.

G. B. Junior, S. V. Rocha, J. D. Almeida, A. C. Paiva, A. C. Silva, and M. Gattass, Breast Cancer Detection in Mammography Using Spatial Diversity, Geostatistics, and Concave Geometry, Multimedia Tools and Applications, vol. 78, pp. 13005-13031, 2019.

B. Ozlu, M. Sahin, K. E. Erdogan, and B. Kocer, The Role of Radiography Guided Non-Palpable Breast Lesion Marking On the Diagnosis of Early Breast Cancer and Proliferative Diseases, Van Tip Derg Medical Journal, vol. 25, no.1 pp. 34-42, 2018.

N. M. Basheer and M. H. Mohammed, Segmentation of Breast Masses in Digital Mammograms Using Adaptive Median Filtering and Texture Analysis, International Journal of Recent Technology and Engineering, vol. 2, no. 1, pp. 39-43, 2013.

A. K. Singh and B. Gupta, A Novel Approach for Breast Cancer Detection and Segmentation in a Mammogram, Procedia Computer Science, vol. 54, pp. 676-682, 2015.

A. P. Singh and B. Singh, Texture Features Extraction in Mammograms Using Non-Shannon Entropies, Machine Learning and Systems Engineering. Lecture Notes in Electrical Engineering, Dordrecht, Springer, pp. 341-351, 2010.

E. A. Sickles, C. J. D'Orsi, and L. W. Bassett, ACR BI-RADS Atlas, Breast Imaging Reporting and Data Systems, Reston: American College Radiology, 2013.

S. Sasikala, M. Ezhilarasi, and S. A. Kumar, Detection of Breast Cancer Using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly Algorithm and Optimum-Path Forest Classifier, Applied Nature-Inspired Computing: Algorithms and Case Studies, Springer Tracts in Nature-Inspired Computing (STNIC), 2020.

J. R. G. Sepulveda, M. T. Cisneros, D. A. M. Arrioja, J. R. Pinales, O. G. I. Manzano, G. A. Cervantes, and A. G. Parada, Digital Image Processing Technique for Breast Cancer Detection, International Journal Thermophys, vol. 34, pp. 1519-1531, 2013.

A. Unni, N. Eg, S. Vinod, and L. S. Nair, Tumour detection in double threshold segmented mammograms using optimized GLCM features fed SVM, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp. 554-559, 2018.

S. Sasikala and M. Ezhilarasi, Fusion of k-Gabor Features from Medio-Lateral-Oblique and Craniocaudal View Mammograms for Improves Breast Cancer Diagnosis, Journal of Cancer Research and Therapeutics, vol. 14, pp. 1036-1041, 2018.

S. A. Taghanaki, Y. Liu, B. Miles, and H. Ghassan, Geometry Based Pectoral Muscle Segmentation from MLO Mammograms Views, IEEE Transactions on Biomedical Engineering, vol. 64, pp. 1-11, 2017.

R. Ferrari, R. Rangayyan, J. Desautels, R. A. Borges, and A. Fere, Automatic Identification of the Pectoral Muscle in Mammograms, IEEE Transaction on Medical Imaging, vol. 23, pp. 232-245, 2004.

P. Shi, J. Zhong, A. Rampun and H. Wang, A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms, Computers in Biology Medicine, vol. 96, pp. 178-188, 2018.

A. R. Beeravolu, S. Azam, M. Jonkman, B. Shanmugam, K. Kannoorpatti, and A. Anwar, Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN, IEEE Access, vol. 9, pp. 33438-33463, 2021.

M. A. Al-masni, M. A. Al-antari, J. M. Park, G. Gi, T. Y. Kim, and P. Rivera, Detection and Classification of the Breast Abnormalities in Digital Mammograms via Regional Convolutional Neural Network, The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, 2017.

K. Akila, L. S. Jayashree, and A. Vasuki, Mammographic Image Enhancement Using Indirect Contrast Enhancement Techniques, Procedia Computer Science, vol. 47, p. 255 – 261, 2015.

S. Chen and A. R. Ramli, Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation, IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, 2003.

P. Carneiro, C. Debs, A. Andrade, and A. Patrocinio, CLAHE Parameters Effects on the Quantitative and Visual Assessment of Dense Breast Mammograms, IEEE Latin America Transactions, vol. 17, no. 5, pp. 851-857, 2019.

Curated Breast Imaging Subset of DDSM,, Accessed on 05/01/2021.

A. A. Kayode, B. S. Afolabi, and B. O. Ibitoye, An Explorative Survey of Image Enhancement Techniques Used in Mammography, International Journal of Computer Science Issues, vol. 12, no. 1, pp. 72-79, 2015.

M. Pawar and S. Talbar, Local entropy maximization based image fusion for contrast enhancement of mammogram, Journal of King Saud University – Computer and Information Sciences, vol. 33, pp. 150-160, 2021.

K. Wu, W. Li, Y. Ge, Y. Chen, Y. Shen, J. Hu, L. Wang, K. Chu, B. Liu, J. Yan, A subpixel edge detection algorithm based on the combination of border following and gray moment, IEEE 10th International Conference on Nano/Molecular Medicine and Engineering (NANOMED), pp. 34-38, 2016.

J. T. D Bertsimas, Introduction to linear optimation, Massachusetts: Athrns Scientific, 1997.

T. M. Fahrudin, I. Syarif, and A. R. Barakbah, Data Mining Approach for Breast Cancer Patient Recovery, EMITTER International Journal of Engineering Technology, vol. 5, no. 1, pp. 36-71, 2017.

N. P. Firdi, T. A. Sardjono, and N. F. Hikmah, Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer, Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 55, pp. 131-142, 2022.

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.