Abstract
Quantum noise is a signal-dependent, Poisson-distributed noise and the dominant noise source in digital mammography. Quantum noise removal or equalization has been shown to be an important step in the automatic detection of microcalcifications. However, it is often limited by the difficulty of robustly estimating the noise parameters on the images. In this study, a nonparametric image intensity transformation method that equalizes quantum noise in digital mammograms is described. A simple Look-Up-Table for Quantum Noise Equalization (LUT-QNE) is determined based on the assumption that noise properties do not vary significantly across the images. This method was evaluated on a dataset of 252 raw digital mammograms by comparing noise statistics before and after applying LUT-QNE. Performance was also tested as a preprocessing step in two microcalcification detection schemes. Results show that the proposed method statistically significantly improves microcalcification detection performance.
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References
Ekpo, E.U., Egbe, N.O., Egom, A.E., McEntee, M.F.: Mammographic breast density: comparison across women with conclusive and inconclusive mammography reports. J. Med. Imaging Radiat. Sci. 1, 55–59 (2015)
Eadie, L.H., Taylor, P., Gibson, A.P.: A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur. J. Radiol. 81(1), e70–e76 (2012)
Romualdo, L., Vieira, M., Schiabel, H., Mascarenhas, N., Borges, L.: Mammographic image denoising and enhancement using the anscombe transformation, adaptive wiener filtering, and the modulation transfer function. J. Digit. Imaging 26(2), 183–197 (2013)
McLoughlin, K.J., Bones, P.J., Karssemeijer, N.: Noise equalization for detection of microcalcification clusters in direct digital mammogram images. IEEE Trans. Med. Imaging 23(3), 313–320 (2004)
Veldkamp, W.J.H., Karssemeijer, N.: Normalization of local contrast in mammograms. IEEE Trans. Med. Imaging 19(7), 731–738 (2000)
van Schie, G., Karssemeijer, N.: Detection of microcalcifications using a nonuniform noise model. In: Krupinski, E.A. (ed.) IWDM 2008. LNCS, vol. 5116, pp. 378–384. Springer, Heidelberg (2008)
Tromans, C.E., Cocker, M.R., Brady, S.M.: Quantification and normalization of x-ray mammograms. Phys. Med. Biol. 57(20), 6519 (2012)
Bria, A., Marrocco, C., Molinara, M., Tortorella, F.: Detecting clusters of microcalcifications with a cascade-based approach. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 111–118. Springer, Heidelberg (2012)
Dengler, J., Behrens, S., Desaga, J.: Segmentation of microcalcifications in mammograms. IEEE Trans. Med. Imaging 12, 634–642 (1993)
El Naqa, I., Yang, W.M.N., Galatsanos, Y.N.P., Nishikawa, R.M.: A support vector machine approach for detection of microcalcifications. IEEE Trans. Med. Imaging 21(12), 1552–1563 (2002)
Yousef, W.A.: Assessing classifiers in terms of the partial area under the ROC curve. Comput. Stat. Data Anal. 64, 51–70 (2013)
Bria, A., Karssemeijer, N., Tortorella, F.: Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications. Med. Image Anal. 18(2), 241–252 (2014)
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Bria, A., Marrocco, C., Mordang, JJ., Karssemeijer, N., Molinara, M., Tortorella, F. (2016). LUT-QNE: Look-Up-Table Quantum Noise Equalization in Digital Mammograms. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_4
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DOI: https://doi.org/10.1007/978-3-319-41546-8_4
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