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

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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