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. 2011;14(Pt 3):562-9.
doi: 10.1007/978-3-642-23626-6_69.

Adaptive multi-cluster fuzzy C-means segmentation of breast parenchymal tissue in digital mammography

Brad Keller  1 , Diane Nathan, Yan Wang, Yuanjie Zheng, James Gee, Emily Conant, Despina Kontos
Affiliations

Adaptive multi-cluster fuzzy C-means segmentation of breast parenchymal tissue in digital mammography

Brad Keller et al. Med Image Comput Comput Assist Interv. 2011.

Abstract

The relative fibroglandular tissue content in the breast, commonly referred to as breast density, has been shown to be the most significant risk factor for breast cancer after age. Currently, the most common approaches to quantify density are based on either semi-automated methods or visual assessment, both of which are highly subjective. This work presents a novel multi-class fuzzy c-means (FCM) algorithm for fully-automated identification and quantification of breast density, optimized for the imaging characteristics of digital mammography. The proposed algorithm involves adaptive FCM clustering based on an optimal number of clusters derived by the tissue properties of the specific mammogram, followed by generation of a final segmentation through cluster agglomeration using linear discriminant analysis. When evaluated on 80 bilateral screening digital mammograms, a strong correlation was observed between algorithm-estimated PD% and radiological ground-truth of r=0.83 (p<0.001) and an average Jaccard spatial similarity coefficient of 0.62. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner.

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Figures

Fig. 1
Fig. 1
Sample Digital Mammograms of BIRADS Categories I–IV in Increasing Order of Density. I) <25% fibroglandular content; II) fibroglandular content between 26–50%; III) fibroglandular content between 51–75%; IV): fibroglandular content >75%.
Fig. 2
Fig. 2
Segmentation algorithm stages for a k=7 mammogram. a) Segmented breast region; b) Normalized breast-pixel intensity histogram with FCM cluster centroids (vertical lines); c) Pixel cluster-membership represented by shading; d) Final dense tissue segmentation.
Fig. 3
Fig. 3
Scatter-plots of Algorithm-Estimated vs. Radiologist-Provided PD% (Left) and Dense-tissue Segmentation Area (Right). Regression-equations, R2, Pearson Correlations, the linear regression line (black) and the identity-lines (dashed-gray) are provided for reference.
Fig. 4
Fig. 4
Comparison between 2-class (top) and adaptive k=6-class (bottom) FCM segmentation of a BIRADS-IV category breast. Left) Breast Mask; Center) Gray-level Histogram with marked FCM-centroids; Right) Final segmentation
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