Name
Abstract | ||
|---|---|---|
We compared computerized methods that incorporate automated lesion characterization and methods for the assessment of the breast parenchymal pattern on mammograms in order to better predict the pathological status of a breast lesion. Computer-extracted mass feature automatically characterized the shape, spiculation, contrast, and margin of each lesion. On the digitized mammogram of the contralateral breast, computer-extracted texture features were automatically extracted to characterize the radiographic breast parenchymal patterns. Three approaches were investigated. A computerized risk-modulated analysis system for mammographic images is expected to improve characterization of lesions by incorporating cancer-risk information into the decision-making process. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1117/12.481395 | Proceedings of SPIE |
Keywords | Field | DocType |
computer-aided diagnosis,digital mammography,risk assessment,image processing,lesion extraction | CAD,Digital mammography,Mammography,Lesion,Breast lesion,Radiography,Radiology,Medicine,Cancer,Computing systems | Conference |
Volume | ISSN | Citations |
5032 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryellen L. Giger | 1 | 393 | 85.89 |
| Li Lan | 2 | 69 | 18.36 |
| Z Huo | 3 | 38 | 8.69 |
| C J Vyborny | 4 | 43 | 8.64 |
| Hui Li | 5 | 45 | 15.48 |