Background intensity independent texture features for assessing breast cancer risk in screening mammograms

Image intensity and texture in screening mammograms are thought to be associated with the risk of breast cancer. Studies on developing automatic breast cancer risk assessment schemes tend to employ texture measures which are correlated to local background intensity. Accordingly, the contribution of texture alone to risk assessment is not known. Here background intensity independent texture measures are used to assess cancer risk. Moreover risk assessment based on background intensity independent texture outperforms intensity dependent texture suggesting that local image background intensity may confound risk assessment. Performance seems to depend on the view of the breast and so suggests that optimizing schemes for different views may improve risk assessment.

[1]  N. Boyd,et al.  Automated analysis of mammographic densities. , 1996, Physics in medicine and biology.

[2]  J. Wolfe Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[3]  D. Vanel The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[4]  N. Petrick,et al.  Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. , 1998, Medical physics.

[5]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  M. Yaffe,et al.  Characterisation of mammographic parenchymal pattern by fractal dimension. , 1990, Physics in medicine and biology.

[7]  N F Boyd,et al.  Automated analysis of mammographic densities and breast carcinoma risk , 1997, Cancer.

[8]  B. Julesz Textons, the elements of texture perception, and their interactions , 1981, Nature.

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

[10]  Michael Brady,et al.  Texture Based Mammogram Classification and Segmentation , 2006, Digital Mammography / IWDM.

[11]  Song-Chun Zhu,et al.  What are Textons? , 2005, Int. J. Comput. Vis..

[12]  Paul Scheunders,et al.  Wavelets for texture analysis, an overview , 1997 .

[13]  A. Naimark,et al.  Are breast patterns a risk index for breast cancer? A reappraisal. , 1977, AJR. American journal of roentgenology.

[14]  B.V. Dasarathy,et al.  A composite classifier system design: Concepts and methodology , 1979, Proceedings of the IEEE.

[15]  Michael Brady,et al.  Breast Density Segmentation Using Texture , 2006, Digital Mammography / IWDM.

[16]  Styliani Petroudi,et al.  Breast density characterization using texton distributions , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[18]  Gobert N. Lee,et al.  Significance of classification scores subsequent to feature selection , 2006, Pattern Recognit. Lett..

[19]  R. Egan,et al.  Breast cancer mammography patterns , 1977, Cancer.

[20]  M Souto,et al.  Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. , 1995, Physics in medicine and biology.

[21]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[22]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .

[23]  Reyer Zwiggelaar,et al.  Mammographic Segmentation and Risk Classification Using a Novel Binary Model Based Bayes Classifier , 2012, Digital Mammography / IWDM.

[24]  Dennis Gabor,et al.  Theory of communication , 1946 .

[25]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[26]  Isabelle E. Magnin,et al.  Mammographic Texture Analysis: An Evaluation Of Risk For Developing Breast Cancer , 1986 .

[27]  Susan M. Astley,et al.  Classification of Breast Tissue by Texture Analysis , 1991, BMVC.

[28]  Murk J. Bottema,et al.  Intensity Independent Texture Analysis in Screening Mammograms , 2012, Digital Mammography / IWDM.

[29]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[31]  A. Miller,et al.  Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. , 1995, Journal of the National Cancer Institute.

[32]  Martin J. Yaffe,et al.  Characterization Of Mammographic Parenchymal Pattern By Fractal Dimension , 1989, Medical Imaging.

[33]  P. Taylor,et al.  Measuring image texture to separate "difficult" from "easy" mammograms. , 1994, The British journal of radiology.

[34]  R. Schapire The Strength of Weak Learnability , 1990, Machine Learning.

[35]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  H M Jensen,et al.  An atlas of subgross pathology of the human breast with special reference to possible precancerous lesions. , 1975, Journal of the National Cancer Institute.

[37]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  F Merletti,et al.  Mammographic features of the breast and breast cancer risk. , 1982, American journal of epidemiology.

[39]  J Whitehead,et al.  The relationship between Wolfe's classification of mammograms, accepted breast cancer risk factors, and the incidence of breast cancer. , 1985, American journal of epidemiology.

[40]  R N Hoover,et al.  Mammographic parenchymal patterns as indicators of breast cancer risk. , 1989, American journal of epidemiology.

[41]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[42]  Chaur-Chin Chen,et al.  Markov random fields for texture classification , 1991, Optics & Photonics.

[43]  Mads Nielsen,et al.  Mammographic Parenchymal Texture Analysis for Estrogen-Receptor Subtype Specific Breast Cancer Risk Estimation , 2012, Digital Mammography / IWDM.

[44]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[45]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[46]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[47]  Song-Chun Zhu,et al.  What are Textons? , 2005, International Journal of Computer Vision.

[48]  Reyer Zwiggelaar,et al.  Mammographic segmentation based on mammographic parenchymal patterns and spatial moments , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[49]  R L Egan,et al.  Breast cancer mammography patterns. , 1977, Cancer.

[50]  V. McCormack,et al.  Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis , 2006, Cancer Epidemiology Biomarkers & Prevention.

[51]  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).

[52]  B. Julesz,et al.  Human factors and behavioral science: Textons, the fundamental elements in preattentive vision and perception of textures , 1983, The Bell System Technical Journal.