Local Feature Based Mammographic Tissue Pattern Modelling and Breast Density Classification
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 computeraided 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 denstiy 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.
mammographic tissue patterns texture analysis breast density classification
Zhili Chen Erika Denton Reyer Zwiggelaar
Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang, 110168, P. R. C Deparment of Breast Imaging Norfolk and Norwich University Hospital Norwich, NR4 7UY, UK Department of Computer Science Aberystwyth University Aberystwyth, SY23 3DB, UK
国际会议
上海
英文
351-355
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）