Abstract
Digital mammography (DM) is commonly used as the breast imaging screening modality. For research based on DM datasets with various sources of x-ray detectors, it is important to evaluate if different detectors could introduce inherent differences in the images analyzed. To determine the extent of such effects, we performed a study to compare the effects of two DM detectors, the GE 2000D and DS, on texture analysis using a validated breast texture phantom (Yaffe et. al, University of Toronto). DM images are acquired in Cranio-Caudal (CC) view, and texture features are generated for both raw and post-processed DM images. Image intensity profiles and texture features are compared between the two detector systems. Our results suggest that there are inherent differences in the images. For raw and processed images, the image intensity cumulative distribution function (CDF) curves reveal that there is a scaling and shifting factor respectively between the two detectors. Image normalization with z-score can reduce detector differences for grey-level intensity and the histogram-based texture features. The differences between co-occurrence and run-length texture features persist after intensity normalization, suggesting that simple z-scoring cannot alleviate all the detector effects, potentially also due to differences in the spatial distribution of the intensity values between the two detectors.
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References
Smith, K.L., Isaacs, C.: Management of women at increased risk for hereditary breast cancer. Breast Disease 27, 51–67 (2006)
Gail, M.H., Brinton, L.A., Byar, D.P., Corle, D.K., Green, S.B., Schairer, C., Mulvihill, J.J.: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J. Natl. Cancer 81(24), 1879–1886 (1989)
Boyd, N.F., Guo, H., Martin, L.J., Sun, L., Stone, J., Fishell, E., Jong, R.A., Hislop, G., Chiarelli, A., Minkin, S., Yaffe, M.J.: Mammographic density and the risk and detection of breast cancer. New England Journal of Medicine 356(3), 227–236 (2007)
Harvey, J.A., Bovbjerg, V.E.: Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk. Radiology 230(1), 29–41 (2004)
Huo, Z., Giger, M.L., Wolverton, D.E., Zhong, W., Cumming, S., Olopade, O.I.: Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. Medical Physics 27(1), 4–12 (2000)
Li, H., Giger, M.L., Olopade, O.I., Margolis, A., Lan, L., Chinander, M.R.: Computerized Texture Analysis of Mammographic Parenchymal Patterns of Digitized Mammograms. Academic Radiology 12(7), 863–873 (2005)
Manduca, A., Carston, M.J., Heine, J.J., Scott, C.G., Pankratz, V.S., Brandt, K.R., et al.: Texture Features from Mammographic Images and Risk of Breast Cancer. Cancer Epidemiol. Biomarkers Prev. 18(3), 837–845 (2009)
Kontos, D., Bakic, P.R., Carton, A.K., Troxel, A.B., Conant, E.F., Maidment, A.D.A.: Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: A preliminary study. Academic Radiology 16(3), 283–298 (2009)
Williams, M.B., Yaffe, M.J., Maidment, A.D., Martin, M.C., Seibert, J.A., Pisano, E.D.: Image quality in digital mammography: image acquisition. Journal of the American College of Radiology 3(8), 589–608 (2006)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3, 610–621 (1973)
Galloway, M.D.: Texture classification using gray level run length. Computer Graphics and Image Processing 4, 172–179 (1975)
Amadasum, M., King, R.: Textural features corresponding to textural properties. IEEE Transactions on Systems Man and Cybernetics 19, 1264–1274 (1989)
Caldwell, C.B., Yaffe, M.J.: Development of an anthropomorphic breast phantom. Medical Physics 17(2), 273–280 (1990)
Zheng, Y., Keller, B., Wang, Y., Tustison, N., Song, G., Bakic, P.R., Maidment, A.D., Conant, E.F., Gee, J.C., Kontos, D.: A Fully-Automated Software Pipeline for Parenchymal Pattern Analysis in Digital Breast Images: Toward the Translation of Imaging Biomarkers in Routine Breast Cancer Risk Assessment. In: Quantitative Imaging Reading Room, the 97th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA) 2011, Chicago, IL (software exhibit) (2011)
Smirnov, N.V.: Tables for estimating the goodness of fit of empirical distributions. Annals. of Mathematical Statistics 19, 279–281 (1948)
Rachev, S.T.: Probability Metrics and Stability of Stochastic Models. JohnWiley & Sons (1991)
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Wang, Y. et al. (2012). A Phantom Study for Assessing the Effect of Different Digital Detectors on Mammographic Texture Features. 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_78
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DOI: https://doi.org/10.1007/978-3-642-31271-7_78
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