The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Breast density segmentation using texture
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Texture based mammogram classification and segmentation
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Improved detection of cancer in screening mammograms by temporal comparison
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Mammographic segmentation and risk classification using a novel binary model based bayes classifier
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
A directional small-scale tissue model for an anthropomorphic breast phantom
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Breast cancer risk prediction via area and volumetric estimates of breast density
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Intensity independent texture analysis in screening mammograms
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Pattern Recognition Letters
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Breast density is a known risk factor for breast cancer. Here two classes of texture features, one based on textons derived from local pixel intensity variation and one based on oriented tissue structure characteristics are measured on different regions of the breast in an effort to clarify the potential contribution of texture independent of local tissue density to estimate breast cancer risk. The region just behind the nipple is found to be the most significant local region for estimating risk, but estimates based on the entire breast perform better. Texton features are found to perform better than features based on oriented tissue structure.