A survey of mass detection in mammograms

被引:0
|
作者
Cheng, HD [1 ]
Shi, XJ [1 ]
Min, R [1 ]
Cai, XP [1 ]
Du, HN [1 ]
Feng, L [1 ]
机构
[1] Utah State Univ, Dept Comp Sci, Logan, UT 84322 USA
来源
PROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCES | 2003年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The estimated sensitivity of radiologists in breast cancer screening is only about 75%, but their performance would be improved if they were prompted with the possible locations of abnormalities. Breast cancer CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very important for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal. Automated mass detection and classification is even more challenging. This paper will survey the methods for mass detection and classification, discussed in the publication and compare the advantages and drawbacks.
引用
收藏
页码:720 / 723
页数:4
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