Automated detection of lung nodules in CT images using shape-based genetic algorithm

Abstract

A shape-based genetic algorithm template-matching (GATM) method is proposed for the detection of nodules with spherical elements. A spherical-oriented convolution-based filtering scheme is used as a pre-processing step for enhancement. To define the fitness function for GATM, a 3D geometric shape feature is calculated at each voxel and then combined into a global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on a clinical dataset of 70 thoracic CT scans (involving 16,800 CT slices) that contains 178 nodules as a gold standard. A total of 160 nodules were correctly detected by the proposed method and resulted in a detection rate of about 90%, with the number of false positives at approximately 14.6/scan (0.06/slice). The high-detection performance of the method suggested promising potential for clinical applications.