Assessment of Content-Based Image Retrieval Approaches for Mammography Based on Breast Density Patterns

This study is trying to assess methods commonly used in content-based image retrieval (CBIR) for screening mammography analysis. A database consists of 12 different BI-RADS classes related to breast density patterns of mammogram patches which are taken from IRMA database is used in this study. Three feature extraction methods, namely grey-level co-occurrence matrix (GLCM), principal component analysis (PCA), and scale-invariant feature transform (SIFT) are being investigated and compared with prior studies. Two retrieval methods are also used in this study, namely k-nearest neighbor (KNN) and mutual information (MI) to measure the similarity between query image and images in database. The result will be evaluated using positive count rate in each query for each class. The result of this study is expected to contribute more towards better Computed-Aided Diagnosis (CADx) and specifically screening mammography analysis in clinical cases.