Automated detection of lung nodules in CT images using shape-based genetic algorithm.
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.
Dehmeshki
Jamshid
J
Medicsight PLC, London, UK. j.dehmeshki@kingston.ac.uk
Ye
Xujiong
X
Lin
Xinyu
X
Valdivieso
Manlio
M
Amin
Hamdan
H
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*