Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.

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

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

[3]  Nico Karssemeijer,et al.  Volumetric breast density estimation from full-field digital mammograms , 2006, IEEE Trans. Medical Imaging.

[4]  Zhimin Huo,et al.  Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. , 2002, Radiology.

[5]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. , 2002, Academic radiology.

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

[7]  Arthur Burgess.  On the noise variance of a digital mammography system. , 2004, Medical physics.

[8]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[9]  J. Otten,et al.  Mammographic breast density and risk of breast cancer: Masking bias or causality? , 1998, European Journal of Epidemiology.

[10]  J. H. Hubbell,et al.  Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest , 1995 .

[11]  N Karssemeijer,et al.  Changes in mammographic breast density and concomitant changes in breast cancer risk. , 1999, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.

[12]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

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

[14]  J. Kaufhold,et al.  A calibration approach to glandular tissue composition estimation in digital mammography. , 2002, Medical physics.

[15]  Imma Boada,et al.  Breast Density Segmentation: A Comparison of Clustering and Region Based Techniques , 2008, Digital Mammography / IWDM.

[16]  Berkman Sahiner,et al.  Computerized image analysis: estimation of breast density on mammograms , 2000, Medical Imaging: Image Processing.

[17]  J. Wolfe,et al.  Mammographic features and breast cancer risk: effects with time, age, and menopause status. , 1995, Journal of the National Cancer Institute.

[18]  Nico Karssemeijer,et al.  Thickness correction of mammographic images by means of a global parameter model of the compressed breast , 2004, IEEE Transactions on Medical Imaging.

[19]  N. Boyd,et al.  Mammographic density and the risk and detection of breast cancer. , 2007, The New England journal of medicine.

[20]  Michael Brady,et al.  Mammographic Image Analysis , 1999, Computational Imaging and Vision.

[21]  R. Warren,et al.  Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form. , 2006, The British journal of radiology.

[22]  C. Boetes,et al.  MRI screening for breast cancer in women with familial or genetic predisposition: design of the Dutch National Study (MRISC) , 2001, Familial Cancer.

[23]  T. R. Fewell,et al.  Molybdenum, rhodium, and tungsten anode spectral models using interpolating polynomials with application to mammography. , 1997, Medical physics.

[24]  M J Yaffe,et al.  X-ray characterization of breast phantom materials. , 1998, Physics in medicine and biology.

[25]  N. Karssemeijer,et al.  A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography. , 2004, Medical physics.

[26]  P. Porter,et al.  Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. , 2000, Journal of the National Cancer Institute.

[27]  M J Yaffe,et al.  Thickness-equalization processing for mammographic images. , 1997, Radiology.

[28]  N. Boyd,et al.  Analysis of mammographic density and breast cancer risk from digitized mammograms. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[29]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[30]  Xiao Hui Wang,et al.  Evaluation of quantitative measures of breast tissue density from mammography with truth from MRI data , 2003, SPIE Medical Imaging.

[31]  N. Boyd,et al.  The quantitative analysis of mammographic densities. , 1994, Physics in medicine and biology.

[32]  Dev P. Chakraborty,et al.  Breast tissue density quantification via digitized mammograms , 2001, IEEE Transactions on Medical Imaging.

[33]  Dan Rico,et al.  A volumetric method for estimation of breast density on digitized screen-film mammograms. , 2003, Medical physics.

[34]  T. Sellers,et al.  Mammographic density, breast cancer risk and risk prediction , 2007, Breast Cancer Research.

[35]  Michael Brady,et al.  A mammographic image analysis method to detect and measure changes in breast density. , 2004, European journal of radiology.

[36]  Li Lan,et al.  Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. , 2007, Academic radiology.

[37]  Nico Karssemeijer,et al.  Thickness correction of mammographic images by anisotropic filtering and interpolation of dense tissue , 2005, SPIE Medical Imaging.

[38]  M L Giger,et al.  Density correction of peripheral breast tissue on digital mammograms. , 1996, Radiographics : a review publication of the Radiological Society of North America, Inc.

[39]  Berkman Sahiner,et al.  Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. , 2004, Medical physics.

[40]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. , 2000, Medical physics.

[41]  R Holland,et al.  High mammographic breast density and its implications for the early detection of breast cancer , 1999, Journal of medical screening.

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

[43]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences. , 2002, Academic radiology.

[44]  B. Chapman,et al.  Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography. , 2003, AJR. American journal of roentgenology.