Skip to main content

MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation

Publication ,  Conference
Mcgurk, R; Bowsher, J; Smith, V; Lee, J; Das, S
Published in: Medical Physics
January 1, 2012

Purpose: Gaussian smoothing reduces noise in PET images but blurs edges impacting the achievable accuracy in radiotherapy target segmentation. Bilateral filters both provide smoothing while simultaneously preserving edges. We compared the accuracy of four methods used to segment objects in filtered images. Methods: Five spherical volumes (1.1–26.5 ml) were imaged for 1, 2 and 5 minutes at high (8:1) and low (4:1) contrast. Four filters were used to smooth images: two Gaussian kernels of 3 mm and 6 mm spatial FWHM, and two bilateral filters with the same FWHM but with an adaptive intensity kernel. Segmentation methods of 40%‐max (40%), adaptive thresholding (ADP), K‐means (KM) and region‐growing (SRG) segmented the volumes. Accuracy was judged comparing segmentations to a known ground truth via Jaccard coefficients (JC) (values closer to 1 better) and symmetric mean average surface distance (SMASD) error. Models describing the impact of filtering and segmentation method were fit using generalized estimating equations. Results: Median JC and SMASD results indicated the 6mm FWHM Gaussian filter was the most accurate across all segmentation techniques (JC=0.72, SMASD=0.68mm). ADP and KM were tied for accuracy across all filters (JC=0.68, SMASD=0.73mm). Model estimated accuracy was significantly higher for each Gaussian filter relative to its corresponding bilateral version and for ADP compared to 40% (p<0.0001). For the 13mm, 4:1 object with 1‐minute scan duration a 6mm FWHM Gaussian kernel with ADP was best (JC=0.59, SMASD=0.95mm). For the 37mm, 8:1 object with 5‐minute scan duration a 6mm FWHM Gaussian kernel with 40% thresholding had the highest JC (0.91) but KM had lower SMASD (0.42mm), vs. 40% (0.5 1mm). Conclusion:Choosing the optimal filter for segmenting radiotherapy targets is a complex relationship between object size, contrast and noise. Object size has the biggest impact. For small low‐contrast objects, Gaussian kernels slightly larger than scanner resolution showed higher accuracy. New Zealand Tertiary Education Commission Top Achiever Doctoral Scholarship. © 2012, American Association of Physicists in Medicine. All rights reserved.

Duke Scholars

Published In

Medical Physics

DOI

ISSN

0094-2405

Publication Date

January 1, 2012

Volume

39

Issue

6

Start / End Page

3881

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Mcgurk, R., Bowsher, J., Smith, V., Lee, J., & Das, S. (2012). MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation. In Medical Physics (Vol. 39, p. 3881). https://doi.org/10.1118/1.4735846
Mcgurk, R., J. Bowsher, V. Smith, J. Lee, and S. Das. “MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation.” In Medical Physics, 39:3881, 2012. https://doi.org/10.1118/1.4735846.
Mcgurk R, Bowsher J, Smith V, Lee J, Das S. MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation. In: Medical Physics. 2012. p. 3881.
Mcgurk, R., et al. “MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation.” Medical Physics, vol. 39, no. 6, 2012, p. 3881. Scopus, doi:10.1118/1.4735846.
Mcgurk R, Bowsher J, Smith V, Lee J, Das S. MO‐G‐BRA‐01: Optimal Image Filtration Strategies for PET Segmentation. Medical Physics. 2012. p. 3881.

Published In

Medical Physics

DOI

ISSN

0094-2405

Publication Date

January 1, 2012

Volume

39

Issue

6

Start / End Page

3881

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences