Abstract
Detection of mass lesions in mammograms via visual readings is a challenging task, and the radiographic density of the breast tissue or its strong anatomical structure may render lesions completely invisible. In order to assess visibility of lesions of a certain size in a given mammogram, we propose a measure for prediction of lesion visibility that complements established approaches for breast density assessment by taking also local structure into account. This measure is based on the analysis of spectral anatomical noise in terms of local standard deviation values for several frequency bands of the mammogram. The resulting values are used to generate two dimensional visibility maps for different lesion sizes. Phantoms of structured tissue equivalent materials were imaged using a full-field digital mammography (FFDM) system, and spherical lesions of different sizes were artificially added to the images. In an observer study with ten observers visibility thresholds were determined from a total of 290 simulated lesions. The resulting nonlinear threshold curve was verified in a second observer study, where 66 lesions were artificially added in clinical mammograms of varying breast density according to BI-RADS classification. A prediction accuracy of 92% was obtained, suffering mostly from different image characteristics in the breast tissue regions near the skinline or the pectoral muscle.
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References
Berg, W.A., Gutierrez, L., NessAiver, M.S., et al.: Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology 233(3), 830–849 (2004)
Burgess, A.E.: Mammographic structure: data preparation and spatial statistics analysis. In: Hanson, K.M. (ed.) Proc. SPIE, Medical Imaging 1999, vol. 3661, pp. 642–653 (1999)
Burgess, A.E., Jacobson, F.L., Judy, P.F.: Human observer detection experiments with mammograms and power-law noise. Med. Phys. 28(4), 419–437 (2001)
D’Orsi, C.J., Bassett, L.W., Berg, W.A., et al.: BI-RADS: Mammography. In: D’Orsi, C.J., Mendelson, E.B., Ikeda, D.M., et al. (eds.) Breast Imaging Reporting and Data System: ACR BI-RADS - Breast Imaging Atlas, 4th edn. American College of Radiology, Reston (2003)
Engstrom, E., Reiser, I., Nishikawa, R.: Comparison of power spectra for tomosynthesis projections and reconstructed images. Med. Phys. 36(5), 1753–1758 (2009)
Fredenberg, E., Svensson, B., Danielsson, M., et al.: Optimization of mammography with respect to anatomical noise. In: Pelc, N.J., Samei, E., Nishikawa, R.M. (eds.) Proc. SPIE, Medical Imaging 2011, vol. 7961, p. 796112 (2011)
Gram, I., Bremnes, Y., Ursin, G., et al.: Percentage density, Wolfe’s and Tabar’s mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 7(5), R854–R861 (2005)
Heese, H., Erhard, K., Goossen, A.: Fully-automatic breast density assessment from full field digital mammograms. In: Tanner, C., et al. (eds.) Proc. BIA 2011, pp. 113–120. Dept. of Computer Science (DIKU), University of Copenhagen (2011)
Heese, H.S., Erhard, K., Goossen, A., et al.: Robust estimation of mammographic breast density: a patient-based approach. In: Haynor, D.R., Ourselin, S. (eds.) Proc. SPIE, Medical Imaging 2012, vol. 8314, p. 83140T (2012)
Heine, J.J., Velthuizen, R.P.: Spectral analysis of full field digital mammography data. Med. Phys. 29(5), 647–661 (2002)
Houssami, N., Kerlikowske, K.: The impact of breast density on breast cancer risk and breast screening. Curr. Breast Cancer Rep. (in print, 2012)
Li, H., Giger, M.L., Olopade, O.I., et al.: Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J. Digit. Imaging 21(2), 145–152 (2008)
McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis. Cancer Epidem. Biomar. 15(6), 1159–1169 (2006)
Stahl, M., Aach, T., Dippel, S.: Digital radiography enhancement by nonlinear multiscale processing. Med. Phys. 27(1), 56–65 (2000)
Wolfe, J.N.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126(6), 1130–1137 (1976)
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Simbt, S., Maack, HI., Heese, H.S. (2012). A Method for Lesion Visibility Prediction in Mammograms by Local Analysis of Spectral Anatomical Noise. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_71
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DOI: https://doi.org/10.1007/978-3-642-31271-7_71
Publisher Name: Springer, Berlin, Heidelberg
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