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Related Experiment Video

Updated: Mar 24, 2026

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Edge map analysis in chest X-rays for automatic pulmonary abnormality screening.

K C Santosh1,2, Szilárd Vajda3, Sameer Antani4

  • 1Department of Computer Science, The University of South Dakota, 414 E Clark St., Vermillion, SD, 57069, USA. santosh.kc@usd.edu.

International Journal of Computer Assisted Radiology and Surgery
|March 21, 2016
PubMed
Summary

This study introduces an automated method to detect pulmonary abnormalities in chest X-rays (CXRs) for tuberculosis screening in HIV+ populations. The technique analyzes thoracic edge maps, achieving high accuracy in resource-limited settings.

Keywords:
AutomationChest X-raysPulmonary abnormalitiesScreeningThoracic edge mapTuberculosis

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Vision

Background:

  • Tuberculosis (TB) screening is crucial for HIV+ populations, especially in resource-constrained regions.
  • Current screening methods may be limited in accessibility and efficiency.

Purpose of the Study:

  • To develop an automated method for detecting pulmonary abnormalities using chest radiographs (CXRs).
  • To facilitate TB screening in HIV+ populations within resource-limited settings.

Main Methods:

  • Analysis of thoracic edge map histograms from posteroanterior chest radiographs (CXRs).
  • Utilized gradient orientations, varying bin numbers and pyramid levels.
  • Employed five distinct regions-of-interest selections.

Main Results:

  • Achieved a maximum abnormality detection accuracy (ACC) of 86.36%.
  • Obtained an area under the ROC curve (AUC) of 0.93.
  • Processed images at an average rate of 1 second per image.

Conclusions:

  • An automated method for pulmonary abnormality screening using CXR thoracic edge maps has been developed.
  • The proposed method demonstrates superior performance compared to existing state-of-the-art techniques.