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IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution.

Menghua Xia1, Wenjun Yan1, Yi Huang1

  • 1Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.

Computers in Biology and Medicine
|May 11, 2019
PubMed
Summary

This study introduces a novel fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method for accurate intravascular ultrasound (IVUS) image segmentation. The FC-HLSE method effectively delineates lumen/MA borders, even with image artifacts, improving plaque burden quantification.

Keywords:
Border detectionHierarchical level set evolutionIntravascular ultrasoundSpatial fuzzy clustering

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Cardiovascular Ultrasound

Background:

  • Accurate segmentation of lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is essential for quantifying plaque burden.
  • Image artifacts present a significant challenge for existing segmentation methods, often leading to inaccurate results.
  • Current complex models struggle to perform reliably on IVUS images affected by artifacts.

Purpose of the Study:

  • To develop an automated method for delineating lumen and MA borders in 20 MHz IVUS frames.
  • To address the challenge of image artifacts in IVUS image segmentation.
  • To improve the accuracy of plaque burden quantification through robust border detection.

Main Methods:

  • A fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method was proposed.
  • Spatial fuzzy c-means (FCM) was used for cluster selection to initialize and regularize the level set evolution (LSE).
  • The method incorporates a two-step LSE process with an intermediate contour extraction stage involving morphological processing, interpolation, and gradient/circular fitting refinement.

Main Results:

  • The FC-HLSE method demonstrated robust performance on 435 publicly available IVUS images, handling both artifact-laden and clean images.
  • Quantitative evaluation using Jaccard measure (JM), Hausdorff distance (HD), and percentage of area difference (PAD) showed high accuracy.
  • Average performance metrics included JM of 0.90/0.89, HD of 0.31/0.40 mm, and PAD of 0.07/0.08 for lumen/MA borders, outperforming some state-of-the-art methods.

Conclusions:

  • The proposed FC-HLSE method provides an effective and automated solution for segmenting lumen and MA borders in IVUS images.
  • The method's ability to handle artifacts significantly enhances its clinical applicability for accurate plaque burden assessment.
  • FC-HLSE offers improved performance compared to existing methods, particularly in challenging imaging conditions.