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CC BY-NC-ND 4.0 · World J Nucl Med 2021; 20(01): 46-53
DOI: 10.4103/wjnm.WJNM_43_20
Original Article

Comparison of noise estimation methods used in denoising 99mTc-sestamibi parathyroid images using wavelet transform

Anil Pandey
Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
,
Param Sharma
1   Department of Computer Science, Sri Guru Tegh Bahadur Khalsa College, University of Delhi, Delhi, India
,
Akshima Sharma
Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
,
Chandra Bal
Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
,
Rakesh Kumar
Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
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Abstract

The objective of this study was to compare the performance of variance, median absolute deviation, and the square of median absolute deviation methods of noise estimation in denoising of 99mTc-sestamibi parathyroid images using wavelet transform. Sixty-eight 99mTc-sestamibi parathyroid images including 33 images acquired at zoom 1.0 and 35 acquired at zoom 2.0 were denoised using the wavethresh package in R. The image decomposition and reconstruction method discrete wavelet transform, wavelet filter db4, shrinkage method hard, and thresholding policy universal were used. The noise estimation in the process was made using var, mad and madmad functions, which use variance, mean absolute deviation, and the square of mean absolute deviation, respectively. The quality of denoised images was assessed both qualitatively and quantitatively. A nonparametric two-sample Kolmogorov–Smirnov test was applied to find whether the difference in image quality produced by these three noise estimation methods was significant at 95% confidence. Noise estimation using madmad function produced the best quality denoised image. Further, the quality of the denoised image using madmad function was significantly better than the quality of the denoised image obtained with var or mad function (P = 1). The estimation of noise using madmad functions in wavelet transforms provides the best-denoised image for both zoom 1.0 and zoom 2.0 99mTc-sestamibi parathyroid images.

Keywords

99mTc-sestamibi parathyroid images - noise estimation methods - wavelet transform

Financial support and sponsorship

Nil.




Publikationsverlauf

Eingereicht: 12. Mai 2020

Angenommen: 25. Mai 2020

Artikel online veröffentlicht:
30. März 2022

© 2021. Sociedade Brasileira de Neurocirurgia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Pizer SM, Todd-Pokropek AE. Improvement of scintigrams by computer processing. Semin Nucl Med 1978;8:125-46.
  • 2 Cinotti L, Meignan M, Usdin JP, Vasile N, Castaigne A. Diagnostic value of image processing in myocardial scintigraphy. J Nucl Med 1983;24:768-74.
  • 3 Hannequin P, Mas J. Statistical and heuristic image noise extraction (SHINE): A new method for processing Poisson noise in scintigraphic images. Phys Med Biol 2002;47:4329-44.
  • 4 Wesolowski CA, Yahil A, Puetter RC, Babyn PS, Gilday DL, Khan MZ. Improved lesion detection from spatially adaptive, minimally complex, Pixon® reconstruction of planar scintigraphic images. Comput Med Imaging Graph 2005;29:65-81.
  • 5 Takalo R, Hytti H, Ihalainen H. Adaptive autoregressive model for reduction of poisson noise in scintigraphic images. J Nucl Med Technol 2011;39:19-26.
  • 6 Gonzalez RC, Woods RE. Intensity transformations and spatial filtering. In: Digital Image Processing. 3rd ed. Delhi: Indian Edition Published by Dorling Kindersley India Pvt. Ltd., licensees of Pearson Education in South Asia; 2009. p. 126-220.
  • 7 Nawres K, Kamel H, Noureddine E. Image denoising using wavelets: A powerful tool to overcome some limitations in nuclear imaging. In: Vol. 1. 2006 2nd International Conference on Information & Communication Technologies; 2006. p. 1114-8.
  • 8 Ogawa K, Sakata M, Li Y. Adaptive noise reduction of scintigrams with a wavelet transform. Int J Biomed Imaging 2012;2012:130482.
  • 9 Afef H, Bechir L, Dorra BS. A based daubechies wavelet transform approach to enhance the OSEM bone SPECT image reconstruction. CEIT-2017. Proceed Eng Technol PET 2018;33:94-100.
  • 10 Bal A, Banerjee M, Sharma P, Maitra M. An efficient wavelet and curvelet-based PET image denoising technique. Med Biol Eng Comput 2019;57:2567-98.
  • 11 Ali R, Yunfeng P, Amin RU. A novel bayesian patch-based approach for image denoising. IEEE Access 2020;8:38985-94.
  • 12 Guy Nason. Wavethresh: Wavelets Statistics and Transforms. Package Version 4.6.8; 2016. Available from: https://CRAN.R-project.org/package=wavethresh. [Last accessed on 2020 May 11].
  • 13 Mallat SG. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Analysis Machine Intelligence 1989;11:674-93.
  • 14 Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21:4695-708.
  • 15 Available from: https://earlglynn.github.io/RNotes/package/waveslim/modwt-2d.html. [Last accessed on 2020 May 11].
  • 16 Buades A, Coll B, Morel JM. A review of image denoising algorithms, with a new one. Multiscale Modeling Simulation 2005;4:490-530.
  • 17 Brunet D, Vrscay ER, Wang Z. The use of residuals in image denoising. In: Kamel M, Campilho A, editors. Image Analysis and Recognition. ICIAR. Lecture Notes in Computer Science. Vol. 5627. Berlin: Springer, Heidelberg; 2009. p.
  • 18 Sheskin DJ. Handbook of Parametric and Nonparametric Statistical Procedures. 4th ed. London, U.K.: Chapman & Hall; 2007.
  • 19 Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. EBImage-an R package for image processing with applications to cellular phenotypes. Bioinformatics 2010;26:979-81.
  • 20 Barthelme S. Imager: Image Processing Library Based on 'CImg'. R Package Version 0.41.1; 2018.
  • 21 Core Team R. A language and environment for statistical computing. Foundation for Statistical Computing, Vienna, Austria. URL; 2018. Available from: https://www.R-project.org/. [Last accessed on 2020 May 11].
  • 22 Donoho DL, Johnstone IM. Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 1995;90:1200-24.
  • 23 Weaver JB, Xu YS, Healy DM Jr., Cromwell LD. Filtering noise from images with wavelet transforms. Magn Reson Med 1991;21:288-95.
  • 24 Wang L, Lu J, Li Y, Yahagi T, Okamoto T. Noise Reduction Using Wavelet with Application to Medical X-Ray Image. In2005 IEEE International Conference on Industrial Technology; 2005. p. 33-8.
  • 25 Wang L, Lu J, Li Y, Yahagi T, Okamoto T. Noise removal for medical X-ray images in wavelet domain. Elect Eng Japan 2008;163:37-46, 22-27.
  • 26 Gonzalez RC, Elwoods R. Digital Image Processing. Reading, MA: Addison-Wesley; 1993. p. 14-23.
  • 27 Pandey AK, Sharma A, Sharma PD, Saroha K, Parida GK, Bal CS, et al. Denoising of iodine-131 images using a median filter. Nucl Med Commun 2019;40:308-16.
  • 28 Pandey AK, Pant GS, Malhotra A. Standardization of SPECT filter parameters. IJNM 2004;19:30-5.
  • 29 Pandey AK, Santosh Y, Sharma PD, Yadav D, Bal C, Kumar R. Restoration of I-131 whole body image using a Wiener filter. Nucl Med Commun 2020;41:426-35.
  • 30 Minarik D, Enqvist O, Trägårdh E. Denoising of scintillation camera images using a deep convolutional neural network: A monte carlo simulation approach. J Nucl Med 2020;61:298-303.
  • 31 Park CR, Kang SH, Lee Y. Median modified Wiener filter for improving the image quality of gamma camera images. Nucl Eng Technol 2020. Volume 52 Issue 10 / Pages.2328-2333.
  • 32 Kim K, Lee MH, Lee Y. Investigation of a blind-deconvolution framework after noise reduction using a gamma camera in nuclear medicine imaging. Nucl Eng Technol 2020. [In press].