Related Experiment Video For Computed tomography
Updated: Jul 20, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Evaluation of an Algorithm for the Segmentation of Lung Nodules in Computerized Tomography Images based on the
Enguo Wang1, Jun Li2, Lei Liu3
1Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, No. 105, Jiefang Road, Jinan, Shandong Province, 250013, China.
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.
