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Related Experiment Video

Updated: May 9, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Improve threshold segmentation using features extraction to automatic lung delimitation.

Cleunio França1, Germano Vasconcelos, Paula Diniz

  • 1Informatics Center - CIn, Federal University of Pernambuco, PE, Brazil.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary

This study introduces an improved lung segmentation algorithm for computed tomography images, achieving 85.5% accuracy. The method enhances basic thresholding using centroid and orientation features for better disease detection.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Processing

Background:

  • Advancements in Picture Archiving and Communication Systems (PACS) and Radiology Information Systems (RIS) have driven progress in medical image analysis algorithms.
  • Current algorithms for tissue segmentation and disease detection show promise but require further refinement in accuracy and processing speed for routine clinical use.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for lung segmentation in X-ray computed tomography (CT) images.
  • To enhance existing threshold segmentation techniques by incorporating feature extraction methods.

Main Methods:

  • The proposed algorithm utilizes feature extraction, including centroid and orientation measures, to improve upon basic threshold segmentation.
  • Applied to X-ray computed tomography images for lung field identification.

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Last Updated: May 9, 2026

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Published on: December 19, 2020

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Main Results:

  • The developed lung segmentation algorithm achieved an accuracy of 85.5%.
  • Feature extraction methods demonstrated an improvement over basic thresholding techniques.

Conclusions:

  • The proposed algorithm shows potential for improving lung segmentation accuracy in CT imaging.
  • Further development is needed to reduce error rates and processing times for clinical applicability.