Name
Abstract | ||
|---|---|---|
Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975). |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.910897 | Proceedings of SPIE |
Keywords | Field | DocType |
mammographic breast density,tissue separation,mammography,FFDM | Histogram,Computer vision,Mammography,Breast cancer,Segmentation,A priori and a posteriori,Artificial intelligence,Region of interest,Calibration,Mixture model,Physics | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 1 |
PageRank | References | Authors |
0.48 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harald S. Heese | 1 | 22 | 4.77 |
| Klaus Erhard | 2 | 6 | 2.39 |
| André Gooßen | 3 | 25 | 6.14 |
| Thomas Bülow | 4 | 252 | 33.08 |