Publication: Exploring graph cut technique for mammography images
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Date
2009-04-01
Authors
Ding, Nik Siong
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Abstract
Breast cancer is one of the most common diseases among woman nowadays. However, the presence of medical imaging tools such as mammography enables
diagnosis of breast cancer to be conducted at beginning stage of breast cancer development. Mammogram is the image produced by mammography process.
The presence of abnormal structures in mammograms will indicate that the woman is having a breast cancer. In this study, a new algorithm called graph cut
was explored to evaluate its efficiency to detect the presence of abnormal structures in mammograms. Although graph cut technique is new but it has
generated a lot interest among the researchers in the computer vision community. The primary reason for this rising popularity has been the successes
of efficient graph cut algorithm in solving many low level vision problems such as image segmentation, object reconstruction, image restoration and disparity
estimation. Masses and mircocalcifications are two most common abnormal structures in mammograms. In this study, a specific mammogram segmentation
algorithm was developed based on the graph cut technique. This mammogram segmentation algorithm was used to test efficiency of the graph cut technique to
segment the abnormal structures out of mammograms. The success rates of detecting these abnormal structures are high. For masses, graph cut technique
able to detect the mass structures in all mammograms used to test this technique. Meanwhile, for microcalcifications, graph cut technique only can detect the microcalcification structures in mammograms which show low density breast. These segmentation results proved that graph cut technique is suitable to
be used to detect the presence of abnormal structures in mammograms.