Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Breast cancer risk prediction via area and volumetric estimates of breast density
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Breast density classification to reduce false positives in CADe systems
Computer Methods and Programs in Biomedicine
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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 cmeans (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