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

Dehmeshki, Jamshid and Ye, Xujiong and Lin, XinYu and Valdivieso, Manlio and Amin, Hamdan (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Computerized Medical Imaging and Graphics, 31 (6). pp. 408-417. ISSN 0895-6111

Full content URL: http://dx.doi.org/10.1016/j.compmedimag.2007.03.00...

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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.

Additional Information: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.
Keywords:Genetic algorithm, Template matching, Lung nodule detection, Computer-aided detection, Shape index
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:7311
Deposited On:22 Jan 2013 17:01

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