Affiliation:
1. HACETTEPE UNIVERSITY
2. HACETTEPE ÜNİVERSİTESİ, TIP FAKÜLTESİ
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.
Publisher
Canakkale Onsekiz Mart University
Reference19 articles.
1. 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
2. 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.
3. Bandyopadhyay, S. (2010). Pre-processing of Mammograms Images. International Journal of Engineering Science and Technology, 2(11), 6753-6758.
4. 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
5. 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.