Optimally Stabilized PET Image Denoising Using Trilateral Filtering

  • Awais Mansoor
  • Ulas Bagci
  • Daniel J. Mollura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks. Conventional PET denoising methods either over-smooth small-sized structures due to resolution limitation or make incorrect assumptions about the noise characteristics. Therefore, clinically important quantitative information may be corrupted. To address these challenges, we introduced a novel approach to remove signal-dependent noise in the PET images where the noise distribution was considered as Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe’s transformation (GAT) was used to stabilize varying nature of the PET noise. Other than noise stabilization, it is also desirable for the noise removal filter to preserve the boundaries of the structures while smoothing the noisy regions. Indeed, it is important to avoid significant loss of quantitative information such as standard uptake value (SUV)-based metrics as well as metabolic lesion volume. To satisfy all these properties, we extended bilateral filtering method into trilateral filtering through multiscaling and optimal Gaussianization process. The proposed method was tested on more than 50 PET-CT images from various patients having different cancers and achieved the superior performance compared to the widely used denoising techniques in the literature.


Positron emission tomography trilateral filtering generalized variance stabilizing transformation denoising 


  1. 1.
    Foster, B., Bagci, U., Mansoor, A., Xu, Z., Mollura, D.J.: A review on segmentation of positron emission tomography images. Computers in Biology and Medicine 50, 76–96 (2014)Google Scholar
  2. 2.
    Sandouk, A., Bagci, U., Xu, Z., Mansoor, A., Foster, B., Mollura, D.J.: Accurate quantification of brown adipose tissue through PET-guided CT image segmentation. Society of Nuclear Medicine Annual Meeting Abstracts 54(suppl. 2), 318 (2013)Google Scholar
  3. 3.
    Chatziioannou, A., Dahlbom, M.: Detailed investigation of transmission and emission data smoothing protocols and their effects on emission images. IEEE Transactions on Nuclear Science 43(1), 290–294 (1996)CrossRefGoogle Scholar
  4. 4.
    Demirkaya, O.: Anisotropic diffusion filtering of PET attenuation data to improve emission images. Physics in Medicine and Biology 47(20), N271 (2002)Google Scholar
  5. 5.
    Dutta, J., Leahy, R.M., Li, Q.: Non-local means denoising of dynamic PET images. PloS One 8(12), e81390 (2013)Google Scholar
  6. 6.
    Hofheinz, F., Langner, J., Beuthien-Baumann, B., Oehme, L., Steinbach, J., Kotzerke, J., van den Hoff, J.: Suitability of bilateral filtering for edge-preserving noise reduction in PET. EJNMMI Research 1(1), 1–9 (2011)CrossRefGoogle Scholar
  7. 7.
    Turkheimer, F.E., Boussion, N., Anderson, A.N., Pavese, N., Piccini, P., Visvikis, D.: PET image denoising using a synergistic multiresolution analysis of structural (mri/ct) and functional datasets. Journal of Nuclear Medicine 49(4), 657–666 (2008)CrossRefGoogle Scholar
  8. 8.
    Bagci, U., Mollura, D.J.: Denoising PET images using singular value thresholding and stein’s unbiased risk estimate. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 115–122. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Anscombe, F.J.: The transformation of poisson, binomial and negative-binomial data. Biometrika 35(3-4), 246–254 (1948)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Starck, J.L., Murtagh, F.D., Bijaoui, A.: Image processing and data analysis: the multiscale approach. Cambridge University Press (1998)Google Scholar
  11. 11.
    Wong, W.C., Chung, A.C., Yu, S.C.: Trilateral filtering for biomedical images. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 820–823. IEEE (2004)Google Scholar
  12. 12.
    Doot, R., Kinahan, P.: SNM lesion phantom report. Technical report, University of Washington (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Awais Mansoor
    • 1
  • Ulas Bagci
    • 1
  • Daniel J. Mollura
    • 1
  1. 1.Department of Radiology and Imaging SciencesNational Institutes of Health (NIH)BethesdaUSA

Personalised recommendations