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

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

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