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 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 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.

[1]  Reyer Zwiggelaar.  Local Greylevel Appearance Histogram Based Texture Segmentation , 2010, Digital Mammography / IWDM.

[2]  Lewis D. Griffin,et al.  Using Basic Image Features for Texture Classification , 2010, International Journal of Computer Vision.

[3]  Vennila Ramalingam,et al.  Automated assessment of breast tissue density in digital mammograms , 2010, Comput. Vis. Image Underst..

[4]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  S. Astley.  Computer-based detection and prompting of mammographic abnormalities. , 2004, The British journal of radiology.

[6]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Robert Marti,et al.  A Comparison of Breast Tissue Classification Techniques , 2006, MICCAI.

[8]  Robert Marti,et al.  A Novel Breast Tissue Density Classification Methodology , 2008, IEEE Transactions on Information Technology in Biomedicine.

[9]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[10]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[11]  Arnau Oliver,et al.  Modeling and Classifying Breast Tissue Density in Mammograms , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Reyer Zwiggelaar,et al.  Mammographic Image Segmentation and Risk Classification Using a Novel Texture Signature Based Methodology , 2010, Digital Mammography / IWDM.

[13]  M. Brady,et al.  Automatic classification of mammographic parenchymal patterns: a statistical approach , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).