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Updated: May 19, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

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Comparative study on the performance of textural image features for active contour segmentation.

Luminita Moraru1, Simona Moldovanu

  • 1Dunarea de Jos University of Galati, Galati, RO-800008, Romania. luminita.moraru@ugal.ro

Science China. Life Sciences
|August 7, 2012
PubMed
Summary

This study introduces a new computerized method for semi-automatic contour detection in ultrasound images. The novel approach combines gray-level and standard deviation parameters for efficient and accurate image segmentation.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Accurate segmentation of ultrasound images is crucial for medical diagnosis.
  • Existing methods for contour detection can be time-consuming and less efficient.

Purpose of the Study:

  • To develop a fast and efficient computerized method for semi-automatic contour detection in ultrasound images.
  • To introduce a novel image function based on parametric active contour models.

Main Methods:

  • A new image function combining gray-level information and standard deviation parameters was developed.
  • The method utilizes parametric active contour models for curve evolution.
  • Segmentation performance was evaluated using the area error rate and compared with the watershed approach.

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Last Updated: May 19, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Main Results:

  • The proposed method achieved low area error rates (8.88% for breast, 10.82% for liver images).
  • Experiments on synthetic, breast, and liver ultrasound images demonstrated promising segmentation results.
  • The method showed efficiency comparable to or better than the watershed approach.

Conclusions:

  • The developed computerized method offers a fast and efficient solution for semi-automatic contour detection in ultrasound imaging.
  • The integration of standard deviation parameters enhances segmentation accuracy.
  • This technique shows potential for improving medical image analysis workflows.