Intensity Independent Texture Analysis in Screening Mammograms

Image texture features for detecting malignant masses in screening mammograms are proposed that are independent of background intensity mean and variation. Subtracting local means and dividing by local standard deviation reveals linear structures of approximately 0.7 mm width in screening mammograms. A simple texture feature calculated from on this derived image is used to demonstrate that texture information associated with the location of cancer is retained in the mean and standard deviation normalized image. Such texture features have the potential to provide evidence of malignancy that better complements intensity based features for detecting breast cancer in screening mammograms.

[1]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[2]  Isabelle Bloch,et al.  Spiculated Lesions and Architectural Distortions Detection in Digital Breast Tomosynthesis Datasets , 2010, Digital Mammography / IWDM.

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

[4]  C. Floyd,et al.  A study on the computerized fractal analysis of architectural distortion in screening mammograms , 2006, Physics in medicine and biology.

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

[6]  Rangaraj M. Rangayyan,et al.  Detection of breast masses in mammograms by density slicing and texture flow-field analysis , 2001, IEEE Transactions on Medical Imaging.

[7]  Michael Brady,et al.  Towards More Realistic Biomechanical Modelling of Tumours under Mammographic Compressions , 2010, Digital Mammography / IWDM.

[8]  Petr Somol,et al.  Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models , 2009, IEEE Transactions on Image Processing.

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

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

[11]  Nico Karssemeijer,et al.  Detection of stellate distortions in mammograms , 1996, IEEE Trans. Medical Imaging.

[12]  P. Undrill,et al.  The use of texture analysis to delineate suspicious masses in mammography. , 1995, Physics in medicine and biology.

[13]  Lubomir M. Hadjiiski,et al.  Improvement of mammographic mass characterization using spiculation meausures and morphological features. , 2001, Medical physics.

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