BioMedical Engineering and Informatics, International Conference on (2008)
May 27, 2008 to May 30, 2008
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.198
Manual segmentation of micro-calcifications in mammogram can provide clinicians with useful information, such as an estimation of the quantification and the size of abnormalities. However, it is a time and labour consuming process. Automatic segmentation has the potential to assist both in the diagnosis of the disease and in treatment planning. This paper presents a novel mammogram image segmentation algorithm that makes use of Scale Invariant Feature Transform (SIFT) to compute the key point in the suspicious area of the mammograms. A database from MIAS is used in this approach. Initial results are presented to show that SIFT can be used to by computing the key-points to segment micro-calcifications of the mammograms. Further work will focus on finding the ways to set the threshold of the segmentation automatically.
Micro-calcification, Mammograms, SIFT, Auto Segmentation
S. Chen, J. Zhang, A. Todd-Pokropek and Q. Guan, "Automatic Segmentation of Micro-calcification Based on SIFT in Mammograms," 2008 International Conference on Biomedical Engineering and Informatics (BMEI 2008)(BMEI), Sanya, 2008, pp. 13-17.