Classification of Microcalcifications in Mammograms using 2D Discrete Wavelet Transform and Random Forest

Breast cancer is the most common form of cancer among women and the leading cause of female deaths from cancer worldwide. Microcalcifications, small crystals of calcium apatites, are considered the first sign of breast cancer. Since microcalcifications are small and have different shapes and low contrast, they can be easily missed or misinterpreted by radiologists. For these reasons, automatic image processing systems are being developed to make the diagnostic process easier for radiologists. In this work, we present a computer-based automated approach for segmentation and classification of breast microcalcifications in mammograms using discrete wavelet transform and random forest (DWT-RF). The proposed approach was tested on 966 images (322 benign, 322, malignant, and 322 normal) from the Digital Database for Screening Mammography. The results indicate that DWT-RF achieves a sensitivity of 93%, a specificity of 97
^%
, a false positive rate of 3%, an accuracy of 95%, and an area under the ROC curve of 0.92, which are comparable in terms of accuracy to state-of-the-art methods and other existing classifiers.