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Updated: Mar 2, 2026

Quantitative Analysis and Characterization of Atherosclerotic Lesions in the Murine Aortic Sinus
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A Color Segmentation-Based Method to Quantify Atherosclerotic Lesion Compositions with Immunostaining.

Congqing Wu1, Alan Daugherty1,2, Hong Lu3,4

  • 1Saha Cardiovascular Research Center, University of Kentucky, Lexington, KY, 40536, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 14, 2017
PubMed
Summary

Atherosclerotic lesion composition, not size, predicts acute events. A new HSI color mode segmentation method offers accurate and objective quantification of lesion composition, improving cardiovascular risk assessment.

Keywords:
AntibodyAtherosclerosisColorImagingImmunostaining

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Area of Science:

  • Cardiovascular Research
  • Pathology
  • Medical Imaging

Background:

  • Acute cardiovascular events are increasingly linked to atherosclerotic plaque composition over size.
  • Immunostaining is a standard technique for analyzing atherosclerotic lesion composition.
  • Existing methods for lesion analysis can be subjective.

Purpose of the Study:

  • To introduce a novel, objective method for quantifying atherosclerotic lesion composition.
  • To improve the accuracy and consistency of atherosclerotic plaque analysis.

Main Methods:

  • A color segmentation approach was developed using the HSI (hue, saturation, and intensity) color model.
  • This method was applied to characterize atherosclerotic lesion compositions.
  • The technique aims to reduce subjectivity in quantitative analysis.

Main Results:

  • The HSI color segmentation approach provides accurate quantifications.
  • The method demonstrates consistency in measuring atherosclerotic lesion components.
  • This technique minimizes observer-dependent variability.

Conclusions:

  • Color segmentation in HSI mode offers a reliable tool for atherosclerotic lesion analysis.
  • Objective quantification of plaque composition can enhance the understanding of acute cardiovascular events.
  • This approach has the potential to improve clinical risk stratification.