Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Analysis of Anatomical Linear Structure Information in Mammographic Risk Assessment
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Automated assessment of breast tissue density in digital mammograms
Computer Vision and Image Understanding
Fuzzy-rough approaches for mammographic risk analysis
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Quantitative assessment of breast dense tissue on mammograms
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MammoSys: A content-based image retrieval system using breast density patterns
Computer Methods and Programs in Biomedicine
The Knowledge Engineering Review
A tree classifier for automatic breast tissue classification based on BIRADS categories
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
A review of breast tissue classification in mammograms
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Fully automated gradient based breast boundary detection for digitized X-ray mammograms
Computers in Biology and Medicine
Local greylevel appearance histogram based texture segmentation
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Automatic breast tissue classification based on BIRADS categories
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Machine learning techniques and mammographic risk assessment
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
Adapting breast density classification from digitized to full-field digital mammograms
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Information Sciences: an International Journal
Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring
Computers in Biology and Medicine
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
Breast density classification to reduce false positives in CADe systems
Computer Methods and Programs in Biomedicine
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It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.