Detection of microcalcification with top-hat transform and the Gibbs random fields

Breast cancer is one of the most common causes of death in women aged 40 and above. Early detection of breast cancer has been one of the prime topics of research in biomedical engineering area. Micro-calcifications (MCs) are the indicators of early stages of breast cancer, and the detection of these MCs will, in turn, lead to diagnosis and treatment of breast cancer at its earliest stages. This paper proposes a new method to detect MCs in a digital mammogram. The approach starts with the segmentation of the digital mammogram to isolate the breast region, using fuzzy C means clustering algorithm. The segmented image is then further segmented using top-hat transform to localize the region of interest. A watershed transform is used to isolate the region of interest from rest of the image. The Gibbs random fields are employed to analyze the pixels in conjunction with the devised clique patterns and detect MCs in the image. A thresholding is performed on the processed image where the MCs are detected. The proposed algorithm is highly effective in reducing the region of interest to the region which has a high probability of finding a calcification or MC. It has an overall detection rate of 94.4% and accuracy of 88.2% with a false negative detection rate of 5.6%, respectively.