Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c‐means clustering and support vector machine segmentation
Abstract
The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer‐aided assessment of breast PD% have been performed using digitized screen‐film mammograms, while digital mammography is increasingly replacing screen‐film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 25, 2012
- Source ID
- 10.1118/1.4736530
Entities
People
- Brad M. Keller
- Despina Kontos
- Diane L. Nathan
- Emily F. Conant
- James C. Gee
- Yan Wang
- Yuanjie Zheng
Organizations
- American Cancer Society
- National Institutes of Health
- United States Department of Defense