Fuzzy Image Processing for An Early Detection of Breast Tumours
DOI:
https://doi.org/10.11113/mjfas.v22n5.4601Keywords:
Fuzzy set theory, Fuzzy Intensification operator, Fuzzy C-Mean clustering, breast cancer, early detectionAbstract
Breast tumour analysis via mammography remains challenging due to tumour appearances, poorly defined boundaries and low contrast differences. Fuzzy set theory, which can handle the ambiguity and intricacies of real-world applications, have shown promising results to overcome the constraints of the mammography imaging. This study incorporated a Fuzzy Intensification (INT) operator for the enhancement process and Fuzzy C-Mean (FCM) clustering for the segmentation process, implemented using MATLAB R2023a. The results of the fuzzy-based methods were compared with the traditional image processing techniques. The results yielded enhanced clarity and contrast of the mammography images, achieving higher Peak-to-Signal Ratio (PSNR) and lower Mean Squared Error (MSE) values. While the FCM-based clustering method successfully segmented tumour regions in some cases, it demonstrated over-segmentation in complex tumour characteristics. The results emphasised the potential for integrating fuzzy image processing techniques into clinical workflows to improve early detection of breast tumours. Future research directions are crucial to maximise the potential of fuzzy image processing in the field of medical imaging.
References
[1] L. Wang, Sensors 17 (2017) 1572.
[2] A. Jain, A. Sharma, J. Crit. Rev. 7 (2020) 1–10.
[3] N. Otsu, IEEE Trans. Syst. Man Cybern. 9 (1979) 62–66.
[4] J. Canny, IEEE Trans. Pattern Anal. Mach. Intell. 8 (1986) 679–698.
[5] A. Norouzi, M.S.M. Rahim, A. Altameem, T. Saba, A.E. Rad, A. Rehman, M. Uddin, IETE Tech. Rev. 31 (2014) 199–213.
[6] C.-C. Zhang, J.-D. Fang, in: Proceedings of the 2016 International Conference on Computer Engineering and Information Systems (CEIS-16), Atlantis Press, Beijing, 2016, p. 129–132.
[7] J.S. Drukteinis, B.P. Mooney, C.I. Flowers, R.A. Gatenby, Am. J. Med. 126 (2013) 479–484.
[8] A. Wadhwa, J.R. Sullivan, M.B. Gonyo, Curr. Probl. Diagn. Radiol. 45 (2016) 397–405.
[9] M.E. Ahmed, M. Sid, A. Aldosh, IOSR J. Dent. Med. Sci. 18 (2019) 55–61.
[10] M. Zhang, B. Mesurolle, M. Theriault, S. Meterissian, E.A. Morris, Curr. Probl. Cancer 47 (2023) 100967.
[11] M. Hazarika, L.B. Mahanta, Asian Pac. J. Cancer Prev. 19 (2018) 2141–2148.
[12] A. Iswardani, W. Hidayat, Int. J. Comput. Sci. Inf. Secur. 16 (2018) 45–50.
[13] H.B. Kekre, S.M. Gharge, Int. J. Comput. Sci. Eng. 2 (2010) 1086–1091.
[14] S. Chakraborty, M.K. Bhowmik, A.K. Ghosh, T. Pal, in: Proceedings of the 2016 IEEE Region 10 Conference (TENCON), IEEE, Singapore, 2016, p. 1241–1245.
[15] H. Deng, X. Sun, M. Liu, C. Ye, X. Zhou, IET Image Process. 10 (2016) 701–709.
[16] V. Magudeeswaran, K. Balasubramanian, Int. J. Biomed. Eng. Technol. 28 (2018) 1–15.
[17] J. Dabass, S. Arora, R. Vig, in: Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, Noida, 2019, p. 556–561.
[18] H.C. Nurshafira, H. Khairunnisa, A.K. Nahrizul, in: Proceedings of the 2019 IEEE 15th International Colloquium on Signal Processing & its Applications (CSPA), IEEE, Kuala Lumpur, 2019, p. 276–280.
[19] J. Dabass, M. Dabass, J. Image Process. Pattern Recognit. Prog. 10 (2023) 1–8.
[20] L. Pourreza, N. Aghazadeh, M. Hashemzadeh, J. Med. Signals Sens. 16 (2026) 120–130.
[21] H.G. Zadeh, J. Haddadnia, O.R. Seryasat, S.M.M. Isfahani, EXCLI J. 15 (2016) 532–550.
[22] Q. Huang, Y. Luo, Q. Zhang, Int. J. Comput. Assist. Radiol. Surg. 12 (2017) 257–272.
[23] M.A. Aswathy, M. Jagannath, Informatics Med. Unlocked 8 (2017) 74–79.
[24] E. Michael, H. Ma, H. Li, F. Kulwa, J. Li, BioMed Res. Int. 2021 (2021) 9962109.
[25] W. Ansar, B. Raza, Curr. Chin. Sci. 3 (2022) 3–22.
[26] A. Altameem, C. Mahanty, R.C. Poonia, A.K.J. Saudagar, R. Kumar, Diagnostics 12 (2022) 1812.
[27] T. Chaira, A.K. Ray, Fuzzy Image Processing and Applications with MATLAB, CRC Press, Boca Raton, 2009.
[28] M. Elena, Fuzzy C-Means Clustering in MATLAB, MathWorks Technical Notes, Natick, 2013.
[29] S. Kantharia, A. Gadgil, S. Cherian, P. Basu, E. Lucas, Atlas of Breast Cancer Early Detection, International Agency for Research on Cancer, Lyon, 2023.
[30] A. Al-Quran, F. Al-Sharqi, A.M. Djaouti, AIMS Math. 10 (2025) 8615–8646.
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