Enhancing the Visibility of Microcalcifications in Breast Tissue Using Morphological Operations and Gaussian Smoothing Techniques: A Phantom Study

Authors

Keywords:

Breast cancer, Microcalcification, Morphological operations, Gaussian elimination, MSE, SNR

Abstract

Breast cancer is one of the major causes of death among women. Microcalcification (MC) deposits could be an early sign of breast cancer. This study aims to investigate image contrast enhancement techniques that facilitate breast cancer diagnosis in dense breast tissues. A polyvinyl alcohol (PVAL) breast phantom, with embedded MCs was produced. Acquired mammogram images were analysed using morphological operations and the Gaussian smoothing techniques to enhance image contrast. The performances of all the filters were measured qualitatively by visual inspection, and quantitatively by evaluating the MSE, PSNR, and SNR. All experiments were conducted on MATLAB R2020a platform. Qualitative analysis showed that the Gaussian Smoothing filter recorded the best performance. Quantitatively, Low values of MSE with high PSNR/SNR depict better image quality enhancement. The closing morphological filter recorded the least mean value of MSE (32.86 ± 1.30) with the highest PSNR and SNR of 32.97 ± 0.20 and 22.91 ± 0.13 respectively. The Gaussian Smoothing filter came second with MSE, PSNR and SNR values equals 287.43 ± 10.65, 23.55 ± 0.17 and 12.83 ± 0.29 respectively. Therefore, the closing morphological filter produced a superior result quantitatively.

Author Biographies

Franca Oyiwoja Okoh

Department of Pure and Applied Physics.

CONUASS 5/3

Norlaili Ahmad Kabir

School of Physics, USM

Senior Lecturer

John Actor Ocheje

Department of Pure and Applied Physics, Federal University Wukari

CONUASS 5/3

Mohd Fahmi Mohd Yusof

School of Health Science, USM

Senior Lecturer

Rafidah Zainon

School of Physics

Professor

Ezekiel Yangde

Department of Pure and Applied Physics

Lecturer II

Moses Ejike Onudibia

Department of Pure and Applied Physics

Senior Lecturer

Dimensions

Akila, K., Jayashree, L. S., & Vasuki, A. (2015). Mammographic image enhancement using indirect contrast enhancement techniques - A comparative study. Procedia Computer Science, 47(C), 255–261. https://doi.org/10.1016/j.procs.2015.03.205

Bandyopadhyay, S. K. (2010). Pre-processing of Mammogram Images. International Journal of Engineering Science and Technology, 2(11), 6753–6758. https://www.researchgate.net/profile/Samir-Bandyopadhyay/publication/50384221_Pre-processing_of_Mammogram_Images/links/09e4150b8513bba519000000/Pre-processing-of-Mammogram-Images.pdf

Bick, U., & Diekmann, F. (2007). Digital mammography: What do we and what don’t we know? European Radiology, 17(8), 1931–1942. https://doi.org/10.1007/s00330-007-0586-1

Bushberg, Jerrold T., Seibert, J. Anthony, Leidholdt, Edwin M., & Boone, J. M. (2011). Signal-to-noise ratio. In C. W. Michell (Ed.), The Essential Physics of Medical Imaging (3rd ed., pp. 91–92). Wolter, Kluwer Lippincott, Williams & Wilkins.

Danladi, C., Mohammed, A. S. & Usman, A. (2025). A Computer Vision-Based Vehicle Speed Monitoring and Reporting System. Nigerian Journal of Physics. 34(3), 53-64. https://doi.org/10.62292/njp.v34i3.2025.382

Dromain, C., Boyer, B., Ferre, R., Canale, S., Delaloge, S., & Balleyguier, C. (2013). Computed-aided diagnosis (CAD) in the detection of breast cancer. Eur J Radiol, 82(3), 417–423. https://doi.org/10.1016/j.ejrad.2012.03.005

Dubey, R. B., Hanmandlu, M., & Gupta, S. K. (2010). A comparison of two methods for the segmentation of masses in the digital mammograms. Computerized Medical Imaging and Graphics, 34(3), 185–191. https://doi.org/10.1016/j.compmedimag.2009.09.002

Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson.

Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing (second). Prentice-Hall.

Gweon, H. M., Youk, J. H., Kim, J. A., & Son, E. J. (2013). Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment. AJR Am J Roentgenol, 201(3), 692–697. https://doi.org/10.2214/AJR.12.10197

Hendrick, R. E., Pisano, E. D., Averbukh, A., Moran, C., Berns, E. A., Yaffe, M. J., Herman, B., Acharyya, S., & Gatsonis, C. (2010). Comparison of acquisition parameters and breast dose in digital mammography and screen-film mammography in the American College of Radiology Imaging Network digital mammographic imaging screening trial. AJR Am J Roentgenol, 194(2), 362–369. https://doi.org/10.2214/AJR.08.2114

Isa, I. S., Sulaiman, S. N., Mustapha, M., & Darus, S. (2015). Evaluating denoising performances of fundamental filters for T2-weighted MRI images. Procedia Computer Science, 60(1), 760–768. https://doi.org/10.1016/j.procs.2015.08.231

Jalalian, A., Mashohor, S. B., Mahmud, H. R., Saripan, M. I., Ramli, A. R., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging, 37(3), 420–426. https://doi.org/10.1016/j.clinimag.2012.09.024

Joseph, A. M., John, M. G., & Dhas, A. S. (2017). Mammogram image denoising filters: A comparative study. 2017 Conference on Emerging Devices and Smart Systems, ICEDSS 2017, March, 184–189. https://doi.org/10.1109/ICEDSS.2017.8073679

Kshema, George, M. J., & Dhas, D. A. S. (2017). Preprocessing filters for mammogram images: A review. 2017 Conference on Emerging Devices and Smart Systems, ICEDSS 2017, August 2018, 1–7. https://doi.org/10.1109/ICEDSS.2017.8073694

Lee, S., Jin Park, S., Jeon, J. M., Lee, M. H., Ryu, D. Y., Lee, E., Kang, S. H., & Lee, Y. (2019). Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study. Optik, 180(November 2018), 569–575. https://doi.org/10.1016/j.ijleo.2018.11.167

Norlaili, A. K., Okoh, O. F., & Mohd Yusof, M. F. (2021). Radiological and Physical Properties of Tissue Equivalent Mammography Phantom: Characterization and Analysis Methods. Radiation Physics and Chemistry, https://doi.org/10.1016/j.radphyschem.2020.109271.

Ponraj, D., & Jenifer, M. (2011). A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer. Journal of Emerging …, 2(12), 656–664.

Reljin, B., Miloševiæ, Z., Stojiæ, T and Reljin, I (2009). Computer aided system for segmentation and visualization of microcalcifications in digital mammograms. Folia Histochem Cytobiol. 47(3), 525-532

Vivona, L., Cascio, D., Fauci, F., & Raso, G. (2014). Fuzzy technique for microcalcifications clustering in digital mammograms. BMC Medical Imaging, 14(1), 1–18. https://doi.org/10.1186/1471-2342-14-23

Zhang, Y., Cheng, H. D., Huang, J., & Tang, X. (2012). An effective and objective criterion for evaluating the performance of denoising filters. Pattern Recognition, 45(7), 2743–2757. https://doi.org/10.1016/j.patcog.2012.01.015

Published

2026-06-18

How to Cite

Okoh, F. O., Kabir, N. A., Ocheje, J. A., Mohd Yusof, M. F., Mohammed, A. S. A., Zainon, R., Yangde, E., & Onudibia, M. E. (2026). Enhancing the Visibility of Microcalcifications in Breast Tissue Using Morphological Operations and Gaussian Smoothing Techniques: A Phantom Study. Nigerian Journal of Physics, 35(3), 46-55. https://doi.org/10.62292/njp.v35i3.2026.518

Issue

Vol. 35 No. 3 (2026): Nigerian Journal of Physics - Vol. 35 No. 3

Section

Articles
Copyright & Licensing

Copyright (c) 2026 Franca Oyiwoja Okoh, Norlaili Ahmad Kabir, John Actor Ocheje, Mohd Fahmi Mohd Yusof, Ahmad Sufril Azlan Mohammed, Rafidah Zainon, Ezekiel Yangde, Moses Ejike Onudibia

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Okoh, F. O., Kabir, N. A., Ocheje, J. A., Mohd Yusof, M. F., Mohammed, A. S. A., Zainon, R., Yangde, E., & Onudibia, M. E. (2026). Enhancing the Visibility of Microcalcifications in Breast Tissue Using Morphological Operations and Gaussian Smoothing Techniques: A Phantom Study. Nigerian Journal of Physics, 35(3), 46-55. https://doi.org/10.62292/njp.v35i3.2026.518

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