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Related Experiment Video

Updated: May 9, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

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Contourlet-based active contour model for PET image segmentation.

M Abdoli1, R A J O Dierckx, H Zaidi

  • 1Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands.

Medical Physics
|August 10, 2013
PubMed
Summary
This summary is machine-generated.

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This study introduces an improved active contour model for segmenting tumors in PET images, enhancing accuracy in tumor volume delineation and quantification, especially for heterogeneous lesions.

Area of Science:

  • Medical Imaging
  • Image Segmentation
  • Computational Biology

Background:

  • Accurate tumor volume delineation and quantification are crucial for PET-guided therapy.
  • Existing PET image segmentation methods struggle with heterogeneous tracer uptake within lesions.
  • Statistical uncertainty (noise) and uptake heterogeneity in PET images present segmentation challenges.

Purpose of the Study:

  • To present an active contour model for improved PET image segmentation.
  • To address limitations of current methods in handling noisy and heterogeneous PET data.
  • To enhance the accuracy of tumor volume delineation and tracer uptake quantification in PET.

Main Methods:

  • Modified Chan-Vese active contour model incorporating anisotropic diffusion filtering (ADF) and contourlet transform.

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  • ADF preserves edges and regional activity, crucial for PET quantification.
  • Contourlet transform enhances tumor-to-background ratio (TBR), improving boundary detection.
  • Careful parameter selection based on tumor heterogeneity and TBR levels ensures robust handling of lesions.

    • Main Results:

    • The proposed method demonstrated superior performance compared to existing techniques in quantitative analysis.
    • Improved Spatial Overlap Index (SOI) from 0.41±0.14 to 0.54±0.12 in a large clinical dataset.
    • Significantly reduced Mean Relative Error (MRE) from 54.23±103.29 to 0.19±16.63.
    • Substantially decreased Mean Classification Error (MCE) from 112.86±69.07 to 60.58±18.43.

    Conclusions:

    • The developed active contour model offers superior tumor segmentation accuracy in PET images.
    • Achieves higher overlap with ground truth/histology and lower errors than other methods.
    • Enables more precise tumor volume delineation for improved clinical applications.