Visual words based approach for tissue classification in mammograms

Idit Diamant; Jacob Goldberger; Hayit Greenspan

doi:10.1117/12.2007885

26 February 2013 Visual words based approach for tissue classification in mammograms

Idit Diamant, Jacob Goldberger, Hayit Greenspan

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867021 (2013) https://doi.org/10.1117/12.2007885
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

The presence of Microcalcifications (MC) is an important indicator for developing breast cancer. Additional indicators for cancer risk exist, such as breast tissue density type. Different methods have been developed for breast tissue classification for use in Computer-aided diagnosis systems. Recently, the visual words (VW) model has been successfully applied for different classification tasks. The goal of our work is to explore VW based methodologies for various mammography classification tasks. We start with the challenge of classifying breast density and then focus on classification of normal tissue versus Microcalcifications. The presented methodology is based on patch-based visual words model which includes building a dictionary for a training set using local descriptors and representing the image using a visual word histogram. Classification is then performed using k-nearest-neighbour (KNN) and Support vector machine (SVM) classifiers. We tested our algorithm on the MIAS and DDSM publicly available datasets. The input is a representative region-of-interest per mammography image, manually selected and labelled by expert. In the tissue density task, classification accuracy reached 85% using KNN and 88% using SVM, which competes with the state-of-the-art results. For MC vs. normal tissue, accuracy reached 95.6% using SVM. Results demonstrate the feasibility to classify breast tissue using our model. Currently, we are improving the results further while also investigating VW capability to classify additional important mammogram classification problems. We expect that the methodology presented will enable high levels of classification, suggesting new means for automated tools for mammography diagnosis support.

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Idit Diamant, Jacob Goldberger, and Hayit Greenspan "Visual words based approach for tissue classification in mammograms", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867021 (26 February 2013); https://doi.org/10.1117/12.2007885

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