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

Hanife Avcı Jale Karakaya

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

Abstract

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.

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

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.
There are 19 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Industrial Biotechnology
Journal Section Makaleler
Authors

Hanife Avcı 0000-0002-1405-9754

Jale Karakaya 0000-0002-7222-7875

Early Pub Date June 21, 2023
Publication Date June 30, 2023
Submission Date November 4, 2022
Published in Issue Year 2023 Volume: 9 Issue: 2

Cite

APA Avcı, H., & Karakaya, J. (2023). Effect of Different Parameter Values for Pre-processing of Using Mammography Images. Journal of Advanced Research in Natural and Applied Sciences, 9(2), 345-354. https://doi.org/10.28979/jarnas.1199343
AMA Avcı H, Karakaya J. Effect of Different Parameter Values for Pre-processing of Using Mammography Images. JARNAS. June 2023;9(2):345-354. doi:10.28979/jarnas.1199343
Chicago Avcı, Hanife, and Jale Karakaya. “Effect of Different Parameter Values for Pre-Processing of Using Mammography Images”. Journal of Advanced Research in Natural and Applied Sciences 9, no. 2 (June 2023): 345-54. https://doi.org/10.28979/jarnas.1199343.
EndNote Avcı H, Karakaya J (June 1, 2023) Effect of Different Parameter Values for Pre-processing of Using Mammography Images. Journal of Advanced Research in Natural and Applied Sciences 9 2 345–354.
IEEE H. Avcı and J. Karakaya, “Effect of Different Parameter Values for Pre-processing of Using Mammography Images”, JARNAS, vol. 9, no. 2, pp. 345–354, 2023, doi: 10.28979/jarnas.1199343.
ISNAD Avcı, Hanife - Karakaya, Jale. “Effect of Different Parameter Values for Pre-Processing of Using Mammography Images”. Journal of Advanced Research in Natural and Applied Sciences 9/2 (June 2023), 345-354. https://doi.org/10.28979/jarnas.1199343.
JAMA Avcı H, Karakaya J. Effect of Different Parameter Values for Pre-processing of Using Mammography Images. JARNAS. 2023;9:345–354.
MLA Avcı, Hanife and Jale Karakaya. “Effect of Different Parameter Values for Pre-Processing of Using Mammography Images”. Journal of Advanced Research in Natural and Applied Sciences, vol. 9, no. 2, 2023, pp. 345-54, doi:10.28979/jarnas.1199343.
Vancouver Avcı H, Karakaya J. Effect of Different Parameter Values for Pre-processing of Using Mammography Images. JARNAS. 2023;9(2):345-54.


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