IWDM 2014: Breast Imaging pp 707-714 | Cite as
A Shearlet-Based Filter for Low-Dose Mammography
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.
Keywords
Low-dose mammography De-noising Shearlet Transform Multiply VST Poisson noisePreview
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References
- 1.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)CrossRefGoogle Scholar
- 2.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)Google Scholar
- 3.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)CrossRefGoogle Scholar
- 4.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)CrossRefGoogle Scholar
- 5.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)CrossRefMathSciNetGoogle Scholar
- 6.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)CrossRefMathSciNetGoogle Scholar
- 7.Easley, G.R., Labate, D., Colonna, F.: Shearlet Based Total Variation for Denoising. IEEE Trans. Image Process. 18(2), 260–268 (2009)CrossRefMathSciNetGoogle Scholar
- 8.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)CrossRefMATHMathSciNetGoogle Scholar
- 9.Donoho, D.L., Johnston, I.M.: Ideal spatial adaptive via wavelet shrinkage. Biometrika 81, 425–455 (1994)CrossRefMATHMathSciNetGoogle Scholar
- 10.Haizhi, H., Hui, S., Chengzhi, D.: Shearlet shrinkage denoising based total variation regularization’. Journal of Image and Graphics 16(2), 168–173 (2011)Google Scholar
- 11.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)CrossRefGoogle Scholar