Name
Abstract | ||
|---|---|---|
We have developed computerized methods for the analysis of lesions that combine results from different imaging modalities, in this case digitized mammograms; and sonograms of the breast, for distinguishing between malignant and benign lesions. The computerized classification method -- applied here to mass lesions seen on both digitized mammograms and sonograms, includes: (1) automatic lesion extraction, (2) automated feature extraction, and (3) automatic classification. The results for both modalities are then merged into an estimate of the likelihood of malignancy. For the mammograms, computer-extracted lesion features include degree of spiculation, margin sharpness, lesion density, and lesion texture. For the ultrasound images, lesion features include margin definition, texture, shape, and posterior acoustic attenuation. Malignant and benign lesions are better distinguished when features from both mammograms and ultrasound images are combined. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1117/12.431141 | Proceedings of SPIE |
Keywords | Field | DocType |
computer-aided diagnosis,digital mammography,ultrasound,breast imaging | Digital mammography,Mammography,Lesion,Breast imaging,Computer-aided diagnosis,Feature extraction,Malignancy,Radiology,Medicine,Ultrasound | Conference |
Volume | ISSN | Citations |
4322 | 0277-786X | 1 |
PageRank | References | Authors |
0.82 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryellen L. Giger | 1 | 393 | 85.89 |
| Z Huo | 2 | 38 | 8.69 |
| Karla Horsch | 3 | 17 | 3.53 |
| edward hendrick | 4 | 2 | 1.22 |
| Luz A. Venta | 5 | 10 | 2.32 |
| C J Vyborny | 6 | 43 | 8.64 |
| ioana r bonta | 7 | 16 | 2.85 |
| Li Lan | 8 | 69 | 18.36 |