Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Computerized detection of breast masses in digitized mammograms
Computers in Biology and Medicine
Use of prompt magnitude in computer aided detection of masses in mammograms
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
A probabilistic approach for the simultaneous mammogram registration and abnormality detection
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Image similarity and asymmetry to improve computer-aided detection of breast cancer
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Exploitation of correspondence between CC and MLO views in computer aided mass detection
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Generation and application of a probabilistic breast cancer atlas
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Joint analysis of multiple mammographic views in CAD systems for breast cancer detection
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
| Hi-index | 0.00 |
Although state-of-the-art CAD systems for breast imaging have a high sensitivity, their practical applicability is limited by the large number of false positive detections. Recently different multiple view strategies were proposed to increase the specificity. So far it was not possible to compare the performance of these methods, because different validation procedures were used. In this paper we validate all multiple view strategies using the same database and CAD system to make a performance comparison possible. Our results show that the performance difference between different multiple view strategies is small. Asymmetry (difference between left and right breast) and the corresponding view (e.g. if we examine the CC view we use additional information from the MLO view) yields equal performance. The performance is slightly worse using a breast atlas. Multiple view features are weaker than single view features (e.g. CAD score), but it is advantageous that multiple view features provide complementary information.