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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Background

Early detection of pulmonary nodules is critical for the clinical diagnosis and management of pulmonary nodules. Computed tomography imaging is currently the best imaging method for detecting pulmonary nodules.

Objective

This study proposes and applies a new thresholding-based method for identifying pulmonary nodules in computed tomography images.

Methods

The proposed method involves segmenting the lung volume and identifying candidate nodules based on their intensity levels, which are higher than those of the lung parenchyma. Reference points on the histogram curve are used to determine a threshold value, and filtering by geometric characteristics is applied to reduce false positives. The performance of the proposed method is evaluated on a training set consisting of 35 nodules distributed among 16 cases with ground truth using the SPIE-AAPM Lung CT Challenge Database and ELCAP Public Lung Image Database.

Results

The proposed method shows a significant reduction in false positives, filtering from an average of 12,380 candidate nodules to 19 detected nodules. The method also demonstrates a sensitivity of 88.6% for detecting pulmonary nodules with an error of 1 nodule in cases where complete detection is not reached.

Conclusion

The proposed thresholding-based method improves the sensitivity of identifying pulmonary nodules in computed tomography images while reducing false positives.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-11
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