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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sainz de Cea, M.V. Yongyi Yang |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA (Sainz de Cea, M.V.; Yongyi Yang) |
| Abstract | Due to variability among different subjects, the detection accuracy of microcalcifications (MC) in mammograms often varies greatly from case to case. Even for a well-developed MC detector, its performance can be hampered by a number of factors ranging from imaging noise to inhomogeneity in the breast tissue. To address this issue, we use a Bayes' risk approach to account for the variability in the detector output, wherein the probability model of the false-positives (FPs) is determined directly from the case under consideration. In the experiment, we demonstrated the proposed approach on a set of 408 mammograms. The results show that it could both improve the uniformity in detection accuracy among different cases and reduce the FP rate by as much as 44.16% with true-positive rate at 85%. |
| Starting Page | 842 |
| Ending Page | 846 |
| File Size | 823393 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479983391 |
| DOI | 10.1109/ICIP.2015.7350918 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-09-27 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Mammography Detectors Training Probability density function Histograms Design automation Bayes' risk Microcalcification detection false positives computer-aided diagnosis (CAD) |
| Content Type | Text |
| Resource Type | Article |
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