Abstract
Interest is growing in the developing automated breast density measures because of its strong association with breast cancer risk. Although a number of automated methods to quantify mammographic and volumetric density appeared, they still have issues with accuracy and reproducibility; there is demand for developing new accurate and automated breast density estimation techniques. The purpose of this paper is to design and to test a new approach for automatically quantifying true volumetric fibroglandular tissue volumes from clinical screening full-field digital mammograms.
The approach consists in building a statistical model using a training set of digital mammograms with known measures of percent fibroglandular tissue volume, breast volume and fibroglandular tissue volume calculated by phantom based calibration method. To derive these measures, we follow the standard procedure in machine learning: feature generation, feature selection, regression classification of outputs, final model building and testing.
The correlation of features to known volumetric breast volumes was analyzed. In addition, the performance of models created from different groups of features were studied. By building a statistical model with 28 degrees of freedom, we achieved an R2=0.83 between the predicted and measured volumetric breast densities for the testing set of 2000 mammograms which were independent of the training set of 2000 images.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Malkov, S., Wang, J., Kerlikowske, K., Cummings, S., Shepherd, J.A.: Single x-ray absorptiometry method for the quantitative mammographic measure of fibroglandular tissue volume. Medical Physics 36(12), 5525–5536 (2009)
Pawluczyk, O., Augustine, B.J., Yaffe, M.J., Rico, D., Yang, J., Mawdsley, G.E., Boyd, N.F.: A volumetric method for estimation of breast density on digitized screen-film mammograms. Med. Phys. 30(3), 352–364 (2003)
Diffey, J., Hufton, A., Astley, S.M.: A new step-wedge for the volumetric measurement of mammographic density. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 1–9. Springer, Heidelberg (2006)
Kaufhold, J., Thomas, J.A., Eberhard, J.W., Galbo, C.E., Trotter, D.E.: A calibration approach to glandular tissue composition estimation in digital mammography. Med. Phys. 29(8), 1867–1880 (2002)
Heine, J.J., Cao, K., Rollison, D.E.: Calibrated measures for breast density estimation. Acad. Radiol. 18, 547–555 (2011)
Highnam, R., Pan, X., Warren, R., Jeffreys, M., Davey Smith, G., Brady, M.: Breast composition measurements using retrospective standard mammogram form (SMF). Phys. Med. Biol. 5111, 2695–2713 (2006)
van Engeland, S., Snoeren, P.R., Huisman, H., Boetes, C., Karssemeijer, N.: Volumetric breast density estimation from fullfield digital mammograms. IEEE Trans. Med. Imaging 25(3), 273–282 (2006)
Zhou, C., Chan, H., Petrick, N., Helvie, M.A., Goodsitt, M.M., Sahiner, B., Hadjiiski, L.M.: Computerized image analysis: Estimation of breast density on mammograms. Med. Phys. 28(6), 1056–1069 (2001)
Kallenberg, M.G., Lokate, M., van Gils, C.H., Karssemeijer, N.: Automatic breast density segmentation: an integration of different approaches. Phys. Med. Biol. 56(9), 2715–2729 (2011)
Kim, Y., Kim, C., Kim, J.: Automated Estimation of Breast Density on Mammogram using Combined Information of Histogram Statistics and Boundary Gradients. In: Proc. of SPIE, vol. 7624, p. 76242F-1 (2010)
Keller, B.M., Nathan, D.L., Wang, Y., Zheng, Y., Gee, J.C., Conant, E.F., Kontos, D.: Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Med. Phys. 39(8), 4903–4917 (2012)
Saidin, N., Mat Sakim, H.A., Ngah, U.K., Shuaib, I.L.: Computer Aided Detection of Breast Density and Mass, and Visualization of Other Breast Anatomical Regions on Mammograms Using Graph Cuts. Computational and Mathematical Methods in Medicine 2013, Article ID 205384, 13 pages (2013)
Heine, J.J., Velthuizen, R.P.: A statistical methodology for mammographic density detection. Med. Phys. 27, 2644–2651 (2000)
Li, J., Szekely, L., Eriksson, L., Heddson, B., Sundbom, A., Czene, K., Hall, P., Humphreys, K.: High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. Breast Cancer Research 14(4), R114 (2012)
Malkov, S., Wang, J., Duewer, F., Shepherd, J.A.: A Calibration Approach for Single-Energy X-ray Absorptiometry Method to Provide Absolute Breast Tissue Composition Accuracy for the Long Term. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 769–774. Springer, Heidelberg (2012)
Li, H., Giger, M.L., Olopade, O., Margolis, A., Lan, L., Chinander, M.: Computerized Texture Analysis of Mammographic Parenchymal Patterns of Digitized Mammograms. Acad. Radiol. 12, 863–873 (2005)
Castella, C., Kinkel, K., Eckstein, M.P., Sottas, P., Verdun, F., Bochud, F.: Semiautomatic Mammographic Parenchymal Patterns Classification Using Multiple Statistical Features. Acad. Radiol. 14, 1486–1499 (2007)
Haralick, R., Shanmugan, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. Syst., Man Cybernet 3, 610–621 (1973)
Caldwell, C.B., Stapleton, S.J., Holdsworth, D.W., Jong, R.A., Weiser, W.J., Cooke, G., Yaffe, M.J.: Characterisation of mammographic parenchymal pattern by fractal dimension. Phys. Med. Biol. 35, 235–247 (1990)
Boone, J.M., Lindfors, K.K., Beatty, C.S., Seibert, J.A.: A breast density index for digital mammograms based on radiologists’ ranking. J. Digit. Imaging 11, 101–115 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Malkov, S., Mahmoudzadeh, A.P., Kerlikowske, K., Shepherd, J. (2014). Automated Volumetric Breast Density Derived by Statistical Model Approach. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)