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 mammographyimaging 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 mammographyimages would be beneficial in terms of direct clinical application and retrospective analysis.Methods:
This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammographyimages as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist.Results:
Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r = 0.82, p < 0.001) and processed (r = 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r = 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's κ ≥ 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm).Conclusions:
The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographicimages. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.
This work was supported in part by American Cancer Society Grant No. RSGHP-CPHPS-119586, United States Department of Defense Concept Award No. BC086591, and National Institutes of Health PROSPR Grant No. 1U54CA163313-01. The authors would also like to thank Dr. Ahmed Ashraf (University of Pennsylvania) and Dr. Martin Yaffe (University of Toronto) for useful discussions pertaining to this work, as well as to Dr. Yaffe for providing Cumulus and the histogram equalization tools.
II. METHODS AND MATERIALS
II.A. Study population and DM image acquisition
II.B. Radiologist estimation of percent breast density
II.C. Automated breast density estimation
II.C.1. Breast tissue area segmentation
II.C.2. Adaptive fuzzy c-means clustering
II.C.3. Cluster classification and percent density calculation
II.D. Statistical analysis of algorithm performance
III.A. Algorithm performance
III.B. Robustness analysis
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