A Method to Detect Breast Cancer Based on Morphological Operation

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Prashengit Dhar 1,*

1. Department of Computer Science and Engineering, Cox’s Bazar City College, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2021.02.03

Received: 7 Aug. 2020 / Revised: 14 Oct. 2020 / Accepted: 5 Nov. 2020 / Published: 8 Apr. 2021

Index Terms

Mammogram image, breast cancer, dilation, opening, detection


Breast cancer is one of the most common cancer in women worldwide. Early detection of breast cancer can lead to better treatment and decrease in mortality. Mammogram image in medical technology, made it easier to analyze breast cancer. Mammography exam is a specialized imaging technique in medical to scan breast which results in mammogram image. Detecting breast cancer earlier, a patient can have several treatment options and also can save live. Early detection of breast cancer can leads to survive 93 percent or greater in the initial five years. This paper proposes a brseast cancer detection method from mammogram image sample by applying morphological operation on gray image rather than binary. Firstly, image is sent for gamma correction. Then it is converted to gray and applied morphological dilation. Again morphological opening operation is formed on the dilated image. Output of dilated and opening operation is then binarized. An AND operation is performed between both binary images. Some post processing like- small area filtering and hole filling task is took place. Then common unwanted object is removed. Finally rest of the region is the desired cancer infected region. Achieved performance is acceptable and satisfactory through the proposed method.

Cite This Paper

Prashengit Dhar, " A Method to Detect Breast Cancer Based on Morphological Operation", International Journal of Education and Management Engineering (IJEME), Vol.11, No.2, pp. 25-31, 2021. DOI: 10.5815/ijeme.2021.02.03


[1]Radiologyinfo.org,―Mammography, URL: https://www.radiologyinfo.org/en/info.cfm?pg=mammo#overview

[2]Lisa Hutchinson, ―Breast cancer: Challenges, controversies, breakthroughs‖, Nature Reviews Clinical Oncology, Vol. (7), pp. 669- 670, December 2010. Doi:10.1038/nrclinonc.2010.192. 

[3]Alasdair McAndrew, ―An Introduction to Digital Image Processing with Matlab Notes for SCM2511 Image Processing 1‖, School of Computer Science and Mathematics Victoria University of Technology, Ch. 7, pp. 137-141, Semester 1, 2004. 

[4]J. W. Xu, T. D. Pham and X. Zhou, ―A double thresholding method forcancer stem cell detection,‖ 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia, 4-6 Sept. 2011, pp. 695-699. 





[9]Samir M. Badawy1., Alaa A. Hefnawy2, Hassan E. Zidan3, and  Mohammed T. GadAllah, Breast Cancer Detection with Mammogram Segmentation: A Qualitative Study,(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, 2017

[10]R. B. Dubey, S. Bhatia, M. Hanmandlu and S. Vasikarla, ―Breast Cancer Segmentation Using Bacterial Foraging Algorithm,‖ 2013 10th International Conference on Information Technology: New Generations, Las Vegas, NV, 2013, pp. 541-545. Doi: 10.1109/ITNG.2013.88.

[11]M. Mustafa, N. A. Omar Rashid and R. Samad, ―BreAst Cancer Segmentation Based On GVF snake,‖ 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, 2014, pp. 928-931. Doi: 10.1109/IECBES.2014.7047647

[12]Abdul Qayyum and A. Basit, ―Automatic breast segmentation and cancer detection via SVM in mammograms,‖ 2016 International Conference on Emerging Technologies (ICET), Islamabad, 2016, pp. 1- 6. Doi: 10.1109/ICET.2016.7813261.

[13]B. K. Gayathri and P. Raajan, ―A Survey of Breast Cancer Detection Based on Image Segmentation Techniques, 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), Kovilpatti, 2016, pp. 1-5.

[14]F. Eddaoudi, F. Regragui, A. Mahmoudi, N. Lamouri, Masses Detection Using SVM Classifier Based on Textures Analysis, Applied Mathematical Sciences, Vol. 5, no. 8, 367 - 379, 2011

[15]Wener Borges Sampaio, Edgar Moraes Diniz, Arist ´ofanes Corrˆea Silva, Anselmo Cardoso De Paiva and Marcelo Gattass, Detection of Masses in Mammogram Images using cnn, Geostatistic Functions and svm, Computers in Biology and Medicine, vol. 41(8), pp. 653–664, (2011)\

[16]Nasseer M. Basheer and Mustafa H. Mohammed, Segmentation of Breast Masses in Digital Mammograms using Adaptive Median Filtering and Texture Analysis, Int. J. Recent Technol. Eng.(IJRTE), vol. 2(1), pp. 2277–3878, (2013).

[17]Shruti Dalmiya, Avijit Dasgupta and Soumya Kanti Datta, Application of Wavelet Based k-means Algorithm in Mammogram Segmentation, International Journal of Computer Applications, vol. 52(15), pp. 15–19, (2012)