Title:Evaluation of an Algorithm for the Segmentation of Lung Nodules in Computerized Tomography Images based on the Automatic Location of a Threshold
Volume: 20
Author(s): Enguo Wang, Jun Li, Lei Liu and Yankun Liu*
Affiliation:
- Department of Medical Imaging Center, Central Hospital Affiliated to Shandong First Medical University, No. 105, Jiefang Road, Jinan, Shandong
Province, 250013, China
Keywords:
Computed tomography, Pulmonary nodule, Detection of pulmonary nodules, Thresholding, Shape based methods, Watersheds methods.
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.
