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Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method.

Xiaoxi Guo1,2, Shaohui Huang3, Xiaozhu Fu4

  • 1Computer Science Department, Xiamen University, Xiamen, China. gxxamy@163.com.

Biomedical Engineering Online
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Summary

This study introduces an improved fuzzy connectedness algorithm for hepatic vessel segmentation, significantly reducing computational cost and enhancing accuracy through an adaptive threshold. The enhanced method offers advantages in detecting vascular edges and improving overall segmentation performance.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Segmentation

Background:

  • Fuzzy connectedness is effective for object extraction but faces challenges in hepatic vessel segmentation, including high computational cost and difficulty in threshold selection.
  • Existing methods require manual thresholding, which can lead to suboptimal segmentation outcomes for hepatic vessels.

Purpose of the Study:

  • To improve the efficiency and accuracy of hepatic vessel segmentation using the fuzzy connectedness method.
  • To address the computational cost and threshold selection issues in fuzzy connectedness-based hepatic vessel segmentation.

Main Methods:

  • An accelerated strategy using a lookup table was developed to reduce connectivity scene calculation time, achieving a speed-up factor greater than 2.
  • A watershed-like method was employed to determine an optimal adaptive threshold for final segmentation, enhancing robustness.
  • The Ostu algorithm was used for affinity relation parameters, and seed assignment with the mean value minimized location-based influence.

Main Results:

  • The lookup table strategy demonstrated efficiency across four datasets for hepatic vessel segmentation.
  • The adaptive threshold determined by the watershed-like method consistently yielded correct segmentation results.
  • The improved fuzzy connectedness method showed advantages over region-growing in detecting vascular edges and segmenting multiple vessel systems.

Conclusions:

  • An enhanced fuzzy connectedness algorithm was proposed for hepatic vessel segmentation.
  • The improved algorithm effectively addresses computational cost and threshold selection challenges.
  • The method demonstrates superior performance in accuracy and robustness for hepatic vessel segmentation.