Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms
This work addresses the need for computer-aided detection to support radiologists in diagnosing breast cancer, but it is incremental as it compares existing segmentation methods.
The paper tackled the problem of detecting microcalcification clusters (MCCs) in mammograms for early breast cancer diagnosis by comparing adaptive threshold and watershed segmentation methods, with results indicating which method is more appropriate for extracting MCCs.
The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.