Name
Title | ||
|---|---|---|
Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization. |
Abstract | ||
|---|---|---|
In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2018.2814058 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Mammography,Standards,Solid modeling,Adaptation models,Transforms,Cancer | Computer vision,Mammography,Data set,Image noise,Nonparametric statistics,Preprocessor,Pixel,Artificial intelligence,Quantum noise,Standard deviation,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 8 | 0278-0062 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Bria | 1 | 57 | 10.63 |
| Claudio Marrocco | 2 | 84 | 17.53 |
| Lucas R. Borges | 3 | 7 | 4.26 |
| M. Molinara | 4 | 38 | 6.29 |
| Agnese Marchesi | 5 | 0 | 0.68 |
| Jan-Jurre Mordang | 6 | 10 | 4.30 |
| Nico Karssemeijer | 7 | 992 | 122.49 |
| Francesco Tortorella | 8 | 8 | 1.10 |