Detection of Micro Calcifications in Mammogram Images Using Texture Analysis and Logistic Regression

Keywords: Breast Cancer, Classification, Image processing, Logistic Regression, Texture Analysis

Abstract

Micro-calcification in the breast is a symptom of breast cancer. Therefore, detection of micro-calcification in mammogram image plays an important role in the early diagnosis of breast cancer. Because the mammogram images are 2-dimensional, different tissues in the breast are seen on top of each other. Therefore, it is a compelling task for radiologists to identify the masses found in mammogram images. There are different methods for detecting micro-calcification in mammogram images. In this study, different image processing techniques were applied on mammogram images and a region of 80x80 pixel was taken from breast tissue. Texture features of this region were extracted using co-occurrence matrix and classified by logistic regression analysis. Classification success of 88% was achieved with the proposed model.

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Published
2019-12-18
How to Cite
[1]
K. Tutuncu and O. Cataltas, “Detection of Micro Calcifications in Mammogram Images Using Texture Analysis and Logistic Regression”, IJISAE, vol. 7, no. 4, pp. 227-231, Dec. 2019.
Section
Research Article