Abstract
We present an experimental work on the denoising of mammogram with Poisson noise. Reviewing the literature, it is found that the denoising performance of the multiresolution tools like wavelet, contourlet and curvelet implemented on mammogram with Poisson noise is unique. The first part of the investigation deals with the confirmation of this exceptional performance with our result. The later half implements the recently developed denoising approach called the Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) to the Poisson noise corrupted mammogram with an objective to improve the peak signal to noise ratio (PSNR) further. The PURE-LET successfully removes Poisson noise better than the traditional mathematical transforms already mentioned. The computation time and PSNR are also evaluated in the perspective of the cycle spinning technique. This validates the applicability and efficiency of the novel denoising strategy in the field of digital mammography.
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
References
Naveed, N., Hussain, A., Arfan, M.J., Choi, T.S.: Quantum and impulse noise filtering from breast mammogram images. Comput. Methods Programs Biomed. 108(3), 1062–1069 (2012)
Görgel, P., Sertbas, A., Ucan, O.N.: A wavelet-based mammographic image denoising and enhancement with homomorphic filtering. J. Med. Syst. 34(6), 993–1002 (2010)
Mencattini, A., Rabottino, G., Salmeri, M., Lojacono, R.: Denoising and enhancement of mammographic images under the assumption of heteroscedastic additive noise by an optimal subband thresholding. Int. J. Wavelets Multiresolut. Inf. Process. 8(5), 713–774 (2010)
Görgel, P., Sertbas, A., Ucan, O.N.: Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme. Comput. Biol. Med. 43(6), 765–774 (2013)
Eltoukhy, M.M., Faye, I., Samir, B.B.: A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 40(4), 384–391 (2010)
Malar, E., Kandaswamy, A., Kirthana, S.S., Nivedhtha, D., Gauthaam, M.: Curvelet image denoising of mammogram images. Int. J. Med. Eng. Inf. 5(1), 60–67 (2013)
Malar, E., Kandaswamy, A., Kirthana, S.S., Nivedhitha, D.: A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms. In: International Conference on Machine Vision and Image Processing (MVIP), pp. 65-68, Taipei, Taiwan (2012)
Luisier, F., Vonesch, C., Blu, T., Unser, M.: Fast interscale wavelet denoising of Poisson–corrupted images. Sig. Process. 90, 415–427 (2010)
Makitalo, M., Foi, A.: Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise. IEEE Trans. Image Process. 22(1) 91–103, (2012)
Makitalo, M., Foi, A.: Optimal inversion of the Anscombe transformation in low-count Poisson Image denoising. IEEE Trans. Image Process. 20(1) 99–109, (2010)
Candès, E.J., Donoho, D.L.: New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Comm. Pure Appl. Math. 57, 219–266 (2004)
Palakkal, S., Prabhu, K.M.M.: Poisson noise removal from images using the fast discrete curvelet transform. In: National Conference on Communications (NCC), pp. 1–5, Bangalore (2011)
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)
Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)
Bamberger, R.H., Smith, M.J.T.: A filter bank for the directional decomposition of images: theory and design. IEEE Trans. Signal Process. 40(4), 882–893 (1992)
Blu, T., Luisier, F.: The SURE-LET approach to image denoising. IEEE Trans. Image Process. 16(11), 2778–2786 (2007)
Luiser, F., Blu, T., Unser, M.: Image denoising in mixed Poisson-Gaussian noise. IEEE Trans. Image Process. 20(3), 696–708 (2011)
Ying, L., Demanet, L., Candes, E.: 3D discrete curvelet transform. SPIE 5914, 351–361 (2005)
Contourlet Toolbox: http://www.mathworks.in/matlabcentral/fileexchange/8837-contourlet-toolbox
Curvelet Toolbox: http://www.curvelet.org
Pure-Let Toolbox: http://www.mathworks.in/matlabcentral/fileexchange/31557-pure-let-for-poisson-image-denoising
Suckling, J., Parker, J., Dance, D.R., Astley, S.M., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.L., Taylor, P., Betal, D., Savage, J.: The mammographic image analysis society digital mammogram database. In: International Workshop on Digital Mammography, pp. 211–221, York, UK (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Saha, M., Naskar, M.K., Chatterji, B.N. (2015). Poisson Noise Removal from Mammogram Using Poisson Unbiased Risk Estimation Technique. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 340. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2247-7_34
Download citation
DOI: https://doi.org/10.1007/978-81-322-2247-7_34
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2246-0
Online ISBN: 978-81-322-2247-7
eBook Packages: EngineeringEngineering (R0)