Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor phase in texture discrimination
Signal Processing
The unified software development process
The unified software development process
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Modeling and Classifying Breast Tissue Density in Mammograms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Automated assessment of breast tissue density in digital mammograms
Computer Vision and Image Understanding
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
Mammographic image classification using histogram intersection
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Computer aided detection in breast imaging: more than perception AID
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Computer Methods and Programs in Biomedicine
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Breast density segmentation using texture
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Towards Computer-Aided Diagnostics of Screening Mammography Using Content-Based Image Retrieval
SIBGRAPI '11 Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images
Automatic breast tissue classification based on BIRADS categories
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Adapting breast density classification from digitized to full-field digital mammograms
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
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This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.