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It has been shown that there is a strong correlation between breast tissue density/patterns and the risk of developing breast cancer. Thus, modelling mammographic tissue patterns is important for quantitative analysis of breast density and computer-aided mammographic risk assessment. In this paper, we first review different local feature based texture representation algorithms, where images are represented as occurrence histograms over a dictionary of local features. Subsequently, we use these approaches to model mammographic tissue patterns based on local tissue appearances in mammographic images. We investigate the performance of different breast tissue representations for breast density classification. The evaluation is based on the full MIAS database using BIRADS ground truth. The obtained classification results are comparable with existing work, which indicates the potential capability of local feature based texture representation in mammographic tissue pattern analysis.