A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Classification of breast tissue by texture analysis
Image and Vision Computing - Special issue: BMVC 1991
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
An improved GVF snake based breast region extrapolation scheme for digital mammograms
Expert Systems with Applications: An International Journal
Fully automated gradient based breast boundary detection for digitized X-ray mammograms
Computers in Biology and Medicine
Information Sciences: an International Journal
Local greylevel appearance histogram based texture segmentation
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Breast mass contour segmentation algorithm in digital mammograms
Computer Methods and Programs in Biomedicine
Breast density classification to reduce false positives in CADe systems
Computer Methods and Programs in Biomedicine
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Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the pectoral muscle has been processed to extract features. In this approach the region of interest is restricted to the breast tissue alone eliminating the artifacts, background and the pectoral muscle. The mammogram images used in this study are from the Mini-MIAS digital database. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Gray level thresholding and connected component labeling is used to eliminate the artifacts and pectoral muscles from the region of interest. Statistical features are extracted from this region which signify the important texture features of breast tissue. These features are fed to the support vector machine (SVM) classifier to classify it into any of the three classes namely fatty, glandular and dense tissue.The classifier accuracy obtained is 95.44%.