Effect of Different Parameter Values for Pre-processing of Using Mammography Images

Year 2023, Volume: 9 Issue: 2, 345 - 354, 30.06.2023

Hanife Avcı , Jale Karakaya

https://doi.org/10.28979/jarnas.1199343

Cited By: 1

Abstract

Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis (CAD) models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods (Ⅰ. Parameter values are Gauss filter 𝜎=3, Laplacian filter 𝜎=0.6 and 𝛼=0.6; Ⅱ. Parameter values are Gauss filter 𝜎=1, Laplacian filter 𝜎=2 and 𝛼=2). In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

Keywords

Computer-Assisted, image enhancement, image processing, machine learning, classification

References

• Al-Najdawi, N., Biltawi, M., & Tedmori, S. (2015). Mammogram image visual enhancement, mass segmentation and classification. Applied Soft Computing, 35, 175-85. DOI: https://doi.org/10.1016/j.asoc.2015.06.029
• Avcı, H., & Karakaya, J. (2021). Mamografi Görüntülerinde Ön işlemede Kullanılan Filtreleme Yöntemleri İçin Belirlenen Farklı Parametre Değerlerinin Sınıflama Başarısına Etkisi. 22.Ulusal ve 5. Uluslararası Biyoistatistik Online Kongresi.
• Bandyopadhyay, S. (2010). Pre-processing of Mammograms Images. International Journal of Engineering Science and Technology, 2(11), 6753-6758.
• Besl, P.J., & Jain, R.C. (1988). Segmentation through variable-order surface fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(2), 167-192. DOI: https://doi.org/ 10.1109/34.3881
• Dasgupta, A., & Wahed, A. (2014). Clinical Chemistry, Immunology and Laboratory Quality Control. Chapter 4-Laboratory Statistics and Quality Control, 47-66. Demirci R. MedPic interface software 2020.
• Ganvir, N.N., & Yadav, D. M. (2019). Filtering Method for Pre-processing Mammogram Images for Breast Cancer Detection. International Journal of Engineering and Advanced Technology, 9(1), (2019), 4222-4229. DOI: https://doi.org/ 10.35940/ijeat.A1623.109119
• George, M.J., & Sankar, S.P. (2017). Efficient pre-processing filters and mass segmentation techniques for mammogram images. IEEE International Conference on Circuits and Sytems, (2017). DOI: https://doi.org/ 10.1109/ICCS1.2017.8326032
• Karakaya, J. (2021). Evaluation of binary diagnostic tests accuracy for medical researches. Turk J Biochem, 46(2), 103-113. DOI: https://doi.org/10.1515/tjb-2020-0337
• MATLAB and Image Processing Toolbox Release 2017b, The MathWorks, Inc., Natick, Massachusetts, United States. Retrieved from https://www.mathworks.com/products/matlab.html.
• Maheshan, C. M., & Kumar, H. P. (2019). Performance of image pre processing filters for noise removal in transformer oil images at different temperatures. SN Applied Sciences, 2(1), 67-73. DOI: https://doi.org/10.1007/s42452-019-1800-x
• Mehdy, M. M., Ng, P. Y., Shair, E. F., Saleh, N. I. Md., & C. Gomes. (2017). Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer. Open Access Hindawi Computational and Mathematical Methods in Medicine, 1-15. DOI: https://doi.org/10.1155/2017/2610628 Paris, S., Hasinoff, S. W., & Kautz, J. (2011). Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid. ACM Transactions on Graphics (TOG), 30(4), 1-12. DOI: https://doi.org/10.1145/2723694
• Ramani, R., Vanitha, S., & Valarmathy, S. (2013). The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images. International Journal of Image, Graphics and Signal Processing, 5(5), 47-54. DOI: https://doi.org/10.5815/ijigsp.2013.05.06
• RStudio Team (2021). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. Retrieved from http://www.rstudio.com.
• Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., & Cardona, A. Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), (2012), pp.676–682. DOI: https://doi.org/10.1038/nmeth.2019
• Scholl, I., Aach, T., Deserno, T. M., & Torsten, K. (2010). Challenges of medical image processing. Comput Sci Res Dev, 26(1), 5-13. DOI: https://doi.org/10.1007/s00450-010-0146-9
• Suckling, J. (1994). The Mammographic Image Analysis Society Digital Mammogram Database. In Exerpta Medica. International Congress Series 1069, York, England, 1069:375-378
• Swathi, C., Anoop, B. K., Dhas, D.A.S., & Sanker, S. P. (2017). Comparison of different image preprocessing methods used for retinal fundus images. Conference on Emerging Devices and Smart Systems (ICEDSS, 2017). DOI: https://doi.org/ 10.1109/ICEDSS.2017.8073677
• Turgut, A.T., Hasırcıoğlu, F., Koşar, U. (2000). Meme Hastalıklarının Tanısında Mamografi. Sted Sürekli Tıp Eğitimi Dergisi, 9 (12)
• Wickham, H., (2016). ggplot2. Elegant Graphics for Data Analysis. New York, NY : Springer-Verlag New York.