Abstract
To improve image quality of low-dose mammography images, we study a new approach of removing Poisson noise from a degraded image in shearlet domain. We first transform Poisson noise into a near Gaussian noise by a shearlet-based multiply variance stabilizing transform (VST). Second, the initial positions of ideal shearlet coefficients are found by thresholding Gaussian noise coefficients. Third, an iterative scheme is proposed to estimate non-noise coefficients from the found initial ideal shearlet coefficients. Finally, the reduced noise image is obtained by the inverse shearlet transform on the estimated coefficients. The main contribution is to combine thresholding and the iterative scheme. A range of experiments demonstrate that the proposed method outperforms the traditional shearlet-based method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sperl, J., Bequé, D., Claus, B., De Man, B., Senzig, B., Brokate, M.: Computer-Assisted Scan Protocol and Reconstruction (CASPAR) —Reduction of Image Noise and Patient Dose. IEEE Transactions on Medical Imaging 29(3), 724–732 (2010)
Jiang, H., Li, W., Liu, Y., Wang, Z., Ma, L.: Comparison Study of Filters for Poisson Noise Removal. In: 2011 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, Tianjin, China, March 1-3, pp. 171–174 (2011)
Wang, J., Lu, H., Wen, J., Liang, Z.: Multiscale Penalized Weighted Least-Squares Sinogram Restoration for Low-Dose X-Ray Computed Tomography. IEEE Transsactions on Biomedical Enineering 55(3), 1022–1031 (2008)
Jiang, H., Wang, Z., Ma, L., Liu, Y., Li, P.: A Novel Method to Improve the Visual Quality of X-ray CR Images. International Journal of Image, Graphics and Signal Processing (IJIGSP) 3(4), 25–31 (2011)
Zhang, B., Fadili, J.M., Starck, J.-L.: Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal. IEEE Trans. Image Process. 17(7), 1108–1903 (2008)
Mäkitalo, M., Foi, A.: Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising. IEEE Transactions on Image Processing 20(1), 99–109 (2011)
Easley, G.R., Labate, D., Colonna, F.: Shearlet Based Total Variation for Denoising. IEEE Trans. Image Process. 18(2), 260–268 (2009)
Easley, G.R., Labate, D., Lim, W.-Q.: Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis 25, 25–46 (2008)
Donoho, D.L., Johnston, I.M.: Ideal spatial adaptive via wavelet shrinkage. Biometrika 81, 425–455 (1994)
Haizhi, H., Hui, S., Chengzhi, D.: Shearlet shrinkage denoising based total variation regularization’. Journal of Image and Graphics 16(2), 168–173 (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
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
Jiang, H., Zhang, Y., Ma, L., Yang, X., Liu, Y. (2014). A Shearlet-Based Filter for Low-Dose Mammography. 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_98
Download citation
DOI: https://doi.org/10.1007/978-3-319-07887-8_98
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07886-1
Online ISBN: 978-3-319-07887-8
eBook Packages: Computer ScienceComputer Science (R0)