Breast density classification using histogram moments of multiple resolution mammograms

Breast density is a strong indicator for breast cancer, which can be assessed by experienced radiologists using mammograms. In this paper, an automatic approach for breast density classification is studied. Mammographic images are pre-processed to separate breast tissues from the background using intensity and morphology-based algorithms. Histograms of multiple resolution mammograms are calculated on the processed images. The statistical moments are retrieved from the multiple resolution histograms, which are employed as the breast density features. The support vector machine (SVM) techniques are implemented onto the feature space to classify the mammograms into different density categories. Experiments on a public dataset verify the performance of the proposed method.

[1]  J Azpiroz-Leehan,et al.  Selection of biorthogonal filters for image compression of MR images using wavelet packets. , 2000, Medical engineering & physics.

[2]  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).

[3]  J. Wolfe.  Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[4]  M. Reddy,et al.  Screening for breast cancer , 2004 .

[5]  C. Byrne,et al.  What is breast density , 2005 .

[6]  Shree K. Nayar,et al.  Multiresolution histograms and their use for recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Reyer Zwiggelaar,et al.  Mammographic Segmentation Based on Texture Modelling of Tabár Mammographic Building Blocks , 2008, Digital Mammography / IWDM.

[8]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[9]  Runsheng Wang,et al.  Texture description based on multiresolution moments of image histograms , 2008 .

[10]  A. Kandaswamy,et al.  Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms , 2007, Comput. Medical Imaging Graph..

[11]  V. Dandolu,et al.  Mammographic breast density. , 2007, The New England journal of medicine.

[12]  Reyer Zwiggelaar,et al.  Mammographic Density Classification using Multiresolution Histogram Information , .

[13]  K.S. Nikita,et al.  Development of an integrated breast tissue density classification software system , 2008, 2008 IEEE International Workshop on Imaging Systems and Techniques.

[14]  Robert M. Nishikawa,et al.  Learning of Perceptual Similarity From Expert Readers for Mammogram Retrieval , 2009, IEEE Journal of Selected Topics in Signal Processing.