Skip to main content
Top
Published in: Journal of Digital Imaging 3/2013

01-06-2013

Statistical Characterization of Portal Images and Noise from Portal Imaging Systems

Authors: Antonio González-López, Juan Morales-Sánchez, Rafael Verdú-Monedero, Jorge Larrey-Ruiz

Published in: Journal of Imaging Informatics in Medicine | Issue 3/2013

Login to get access

Abstract

In this paper, we consider the statistical characteristics of the so-called portal images, which are acquired prior to the radiotherapy treatment, as well as the noise that present the portal imaging systems, in order to analyze whether the well-known noise and image features in other image modalities, such as natural image, can be found in the portal imaging modality. The study is carried out in the spatial image domain, in the Fourier domain, and finally in the wavelet domain. The probability density of the noise in the spatial image domain, the power spectral densities of the image and noise, and the marginal, joint, and conditional statistical distributions of the wavelet coefficients are estimated. Moreover, the statistical dependencies between noise and signal are investigated. The obtained results are compared with practical and useful references, like the characteristics of the natural image and the white noise. Finally, we discuss the implication of the results obtained in several noise reduction methods that operate in the wavelet domain.
Literature
1.
go back to reference Herman MG, Balter JM, Jaffray DA, McGee KP, Munro P, Shalev S, Van Herk M, Wong JW: Clinical use of electronic portal imaging. Report of AAPM Radiation Therapy Committee Task Group 58. Med Phys 28(5):712–737, 2001PubMedCrossRef Herman MG, Balter JM, Jaffray DA, McGee KP, Munro P, Shalev S, Van Herk M, Wong JW: Clinical use of electronic portal imaging. Report of AAPM Radiation Therapy Committee Task Group 58. Med Phys 28(5):712–737, 2001PubMedCrossRef
2.
go back to reference Antonuk LE: Electronic portal imaging devices: a review and historical perspective of contemporary technologies and research. Phys Med Biol 47:R31–R65, 2002PubMedCrossRef Antonuk LE: Electronic portal imaging devices: a review and historical perspective of contemporary technologies and research. Phys Med Biol 47:R31–R65, 2002PubMedCrossRef
3.
go back to reference Weaver JB, Xu Y, Healy D, Driscoll J: Filtering noise from images with wavelet transforms. Magn Reson Med 21(2):288–295, 1991PubMedCrossRef Weaver JB, Xu Y, Healy D, Driscoll J: Filtering noise from images with wavelet transforms. Magn Reson Med 21(2):288–295, 1991PubMedCrossRef
4.
go back to reference Unser M, Aldroubi A, Laine A: Special issue on wavelets in medical imaging. IEEE Trans Med Imaging, 2003 Unser M, Aldroubi A, Laine A: Special issue on wavelets in medical imaging. IEEE Trans Med Imaging, 2003
5.
go back to reference Donoho DL, Johnstone TM: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455, 1994CrossRef Donoho DL, Johnstone TM: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455, 1994CrossRef
6.
go back to reference Simoncelli EP, Adelson EH: Noise removal via Bayesian wavelet coring. In Proc. IEEE Internat. Conf. Image Proc. (ICIP): 379–382,1996 Simoncelli EP, Adelson EH: Noise removal via Bayesian wavelet coring. In Proc. IEEE Internat. Conf. Image Proc. (ICIP): 379–382,1996
7.
go back to reference Portilla J, Strela V, Wainwright MJ, Simoncelli EP: Image denoising using a scale mixture of Gaussians in the wavelet domain. IEEE Trans Image Process 12(11):1338–1351, 2003PubMedCrossRef Portilla J, Strela V, Wainwright MJ, Simoncelli EP: Image denoising using a scale mixture of Gaussians in the wavelet domain. IEEE Trans Image Process 12(11):1338–1351, 2003PubMedCrossRef
8.
go back to reference Ferrari RJ, Winsor R: Digital radiographic image denoising via wavelet-based hidden Markov model estimation. J Digit Imaging 18(2):154–167, 2005PubMedCrossRef Ferrari RJ, Winsor R: Digital radiographic image denoising via wavelet-based hidden Markov model estimation. J Digit Imaging 18(2):154–167, 2005PubMedCrossRef
9.
go back to reference González A, Morales J, Verdú R, Larrey J, Sancho JL, Tobarra B: SURE-LET and BLS-GSM wavelet-based denoising algorithms versus linear local Wiener estimator in radiotherapy portal image denoising. In Proc. IFMBE World Congress on Medical Physics and Biomedical Engineering. Springer Berlin Heidelberg, 2009 González A, Morales J, Verdú R, Larrey J, Sancho JL, Tobarra B: SURE-LET and BLS-GSM wavelet-based denoising algorithms versus linear local Wiener estimator in radiotherapy portal image denoising. In Proc. IFMBE World Congress on Medical Physics and Biomedical Engineering. Springer Berlin Heidelberg, 2009
10.
go back to reference Bhaudaria H, Dewal M: Efficient denoising technique for CT images to enhance brain hemorrhage segmentation. Journal of Digital Imaging 25:1–10, 2012CrossRef Bhaudaria H, Dewal M: Efficient denoising technique for CT images to enhance brain hemorrhage segmentation. Journal of Digital Imaging 25:1–10, 2012CrossRef
11.
go back to reference Oppenheim AV, Schafer RW, Buck JR: Discrete-time signal processing, 2nd edition. New Jersey: Prentice-Hall, 1999 Oppenheim AV, Schafer RW, Buck JR: Discrete-time signal processing, 2nd edition. New Jersey: Prentice-Hall, 1999
12.
go back to reference Simoncelli E, Olshausen B: Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216, 2001PubMedCrossRef Simoncelli E, Olshausen B: Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216, 2001PubMedCrossRef
13.
go back to reference Donoho DL, Johnstone TM: Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224, 1995CrossRef Donoho DL, Johnstone TM: Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224, 1995CrossRef
14.
go back to reference Sendur L, Selesnick IW: Bivarite shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans Image Process 50(11):2744–2756, 2002CrossRef Sendur L, Selesnick IW: Bivarite shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans Image Process 50(11):2744–2756, 2002CrossRef
15.
go back to reference Stein C: Estimation of the mean of a multivariate normal distribution. Ann Stat 9(6):1135–1151, 1981CrossRef Stein C: Estimation of the mean of a multivariate normal distribution. Ann Stat 9(6):1135–1151, 1981CrossRef
16.
go back to reference Luisier F, Blu T, Unser M: A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 16(3):593–606, 2007PubMedCrossRef Luisier F, Blu T, Unser M: A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 16(3):593–606, 2007PubMedCrossRef
17.
go back to reference Blu T, Luisier F: The SURE-LET approach to image denoising. IEEE Trans Image Process 16(11):2778–2786, 2007PubMedCrossRef Blu T, Luisier F: The SURE-LET approach to image denoising. IEEE Trans Image Process 16(11):2778–2786, 2007PubMedCrossRef
18.
go back to reference Delpretti S, Luisier F, Ramani S, Blu T, Unser M: Multiframe sure-let denoising of time lapse fluorescence microscopy images. In Proceedings of ISBI:149–152, 2008 Delpretti S, Luisier F, Ramani S, Blu T, Unser M: Multiframe sure-let denoising of time lapse fluorescence microscopy images. In Proceedings of ISBI:149–152, 2008
19.
go back to reference Wainwright MJ, Simoncelli EP, Willsky AS: Random cascades on wavelet trees and their use in modelling and analyzing natural imagery. Appl Comput Harmon Anal 11(1):89–123, 2001CrossRef Wainwright MJ, Simoncelli EP, Willsky AS: Random cascades on wavelet trees and their use in modelling and analyzing natural imagery. Appl Comput Harmon Anal 11(1):89–123, 2001CrossRef
20.
go back to reference Field DJ: Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4:2379–2394, 1987CrossRef Field DJ: Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4:2379–2394, 1987CrossRef
21.
go back to reference Ruderman DL, Bialek W: Statistics of natural images: scaling in the woods. Phys Rev Lett 73(6):814–817, 1994PubMedCrossRef Ruderman DL, Bialek W: Statistics of natural images: scaling in the woods. Phys Rev Lett 73(6):814–817, 1994PubMedCrossRef
22.
go back to reference Torralba A, Oliva A: Statistics of natural image categories. Comput Neural Syst 14(3):391–412, 2003CrossRef Torralba A, Oliva A: Statistics of natural image categories. Comput Neural Syst 14(3):391–412, 2003CrossRef
Metadata
Title
Statistical Characterization of Portal Images and Noise from Portal Imaging Systems
Authors
Antonio González-López
Juan Morales-Sánchez
Rafael Verdú-Monedero
Jorge Larrey-Ruiz
Publication date
01-06-2013
Publisher
Springer-Verlag
Published in
Journal of Imaging Informatics in Medicine / Issue 3/2013
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-012-9516-0

Other articles of this Issue 3/2013

Journal of Digital Imaging 3/2013 Go to the issue