Name
Abstract | ||
|---|---|---|
The purpose of this study was to investigate feasibility of computer-aided detection of masses and calcification clusters in breast tomosynthesis images and obtain reliable estimates of sensitivity and false positive rate on an independent test set. Automatic mass and calcification detection algorithms developed for film and digital mammography images were applied without any adaptation or retraining to tomosynthesis projection images. Test set contained 36 patients including 16 patients with 20 known malignant lesions, 4 of which were missed by the radiologists in conventional mammography images and found only in retrospect in tomosynthesis. Median filter was applied to tomosynthesis projection images. Detection algorithm yielded 80% sensitivity and 5.3 false positives per breast for calcification and mass detection algorithms combined. Out of 4 masses missed by radiologists in conventional mammography images, 2 were found by the mass detection algorithm in tomosynthesis images. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.712729 | Proceedings of SPIE |
Keywords | DocType | Volume |
computer-aided diagnosis,mammography,tomosynthesis | Conference | 6514 |
ISSN | Citations | PageRank |
0277-786X | 1 | 0.43 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna K. Jerebko | 1 | 71 | 8.94 |
| yuan quan | 2 | 1 | 0.43 |
| nicolas merlet | 3 | 1 | 0.43 |
| eli ratner | 4 | 1 | 0.43 |
| Swatee Singh | 5 | 11 | 2.84 |
| Joseph Y. Lo | 6 | 261 | 27.03 |
| Arun Krishnan | 7 | 220 | 14.86 |