Breast tissue density is an important risk factor in the detection of breast cancer. It is also known that interpretation of mammogram lesions is more difficult in dense tissues. Therefore, getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis. This article reviews several classification techniques for two datasets, both digitized screen-film (SFM) and full-field digital (FFDM) mammography, classified according to BIRADS categories. It concludes with a tree classification procedure based on the combination of two classifiers on texture features. Statistical analysis to test the normality and homoscedasticity of the features was carried. Thus, just features that are significant influenced by the tissue type were considered. The results obtained on 322 mammograms of the SFM dataset and on 1137 mammograms of the FFDM dataset demonstrate that up to 80% of samples were correctly classified using using 10-fold cross-validation to train and test the classifiers.
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- A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS Categories
José A. Seone
- Springer Berlin Heidelberg
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