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.

[1]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[2]  L. Schwartz,et al.  Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm , 2003, Journal of applied clinical medical physics.

[3]  Li Fan,et al.  Automatic detection of lung nodules from multislice low-dose CT images , 2001, SPIE Medical Imaging.

[4]  Taylor Murray,et al.  Cancer statistics, 2000 , 2000, CA: a cancer journal for clinicians.

[5]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.

[6]  S. Armato,et al.  Automated detection of lung nodules in CT scans: preliminary results. , 2001, Medical physics.

[7]  Kang-Ping Lin,et al.  Object-based deformation technique for 3D CT lung nodule detection , 1999, Medical Imaging.

[8]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[9]  Olivier Monga,et al.  Using Partial Derivatives of 3D Images to Extract Typical Surface Features , 1995, Comput. Vis. Image Underst..

[10]  Takeo Ishigaki,et al.  Improvement in automated detection of pulmonary nodules on helical x-ray CT images , 2004, SPIE Medical Imaging.

[11]  Ayman El-Baz,et al.  Automatic Detection and Recognition of Lung Abnormalities in Helical CT Images Using Deformable Templates , 2004, MICCAI.

[12]  M. McNitt-Gray,et al.  Patient-specific models for lung nodule detection and surveillance in CT images , 2001, IEEE Transactions on Medical Imaging.

[13]  O. Monga,et al.  Using partial Derivatives of 3D images to extract typical surface features , 1992, Proceedings of the Third Annual Conference of AI, Simulation, and Planning in High Autonomy Systems 'Integrating Perception, Planning and Action'..

[14]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[15]  Lubomir M. Hadjiiski,et al.  Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. , 2005, Medical physics.

[16]  Jin Mo Goo,et al.  Computer-Aided Detection of Lung Nodules on Chest CT: Issues to be Solved before Clinical Use , 2005, Korean journal of radiology.

[17]  Noboru Niki,et al.  Computer-aided diagnosis system for lung cancer based on retrospective helical CT image , 2000, Medical Imaging: Image Processing.

[18]  E. Kazerooni,et al.  Computer-aided detection of lung nodules : False positive reduction using a 3 D gradient field method and 3 D ellipsoid fitting , 2005 .

[19]  Noboru Niki,et al.  Computer-aided diagnosis system for lung cancer based on retrospective helical CT images , 1999, Medical Imaging.

[20]  A. Baert,et al.  [High-resolution CT of the lung]. , 1991, Rontgenpraxis; Zeitschrift fur radiologische Technik.

[21]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[22]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[23]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[24]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[25]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.