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Related Experiment Video

Updated: Jul 2, 2026

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
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Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

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Automatic segmentation of liver PET images.

Chih-Yu Hsu1, Chun-You Liu, Chung-Ming Chen

  • 1Department of Information and Communication Engineering, Chaoyang University of Technology, Wufeng, Taichung County, Taiwan, ROC. tccnchsu@gmail.com

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|August 30, 2008
PubMed
Summary

This study introduces a new method for automating liver segmentation in positron emission tomography (PET) images. The Poisson Gradient Vector Flow active contour model with a genetic algorithm successfully segmented liver regions in both normal and abnormal patient data.

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Image Processing

Background:

  • Positron emission tomography (PET) is crucial in clinical diagnostics.
  • Liver segmentation in PET images is challenging due to image quality limitations.
  • Accurate liver segmentation is vital for quantitative analysis and treatment planning.

Purpose of the Study:

  • To develop an automated method for liver segmentation in PET images.
  • To introduce a novel active contour model (ACM) for improved segmentation accuracy.
  • To evaluate the proposed method on diverse liver PET datasets.

Main Methods:

  • Development of a Poisson Gradient Vector Flow (PGVF) active contour model.
  • Integration of a genetic algorithm (GA) to optimize the segmentation process.

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  • Testing the automated segmentation scheme on three distinct PET image datasets (one normal, two abnormal livers).

    • Main Results:

    • The proposed PGVF-GA model successfully automated liver segmentation in all tested PET image datasets.
    • Accurate identification of liver regions of interest (ROI) was achieved.
    • The method demonstrated effectiveness across varying liver conditions.

    Conclusions:

    • The developed PGVF-ACM with GA offers an effective solution for automated liver segmentation in PET imaging.
    • This approach can overcome challenges associated with low-quality PET images.
    • The automated segmentation shows promise for clinical applications and research.