Computer simulation for segmentation of lung nodules in CT images

Automated lung nodule detection through computed tomography (CT) image segmentation is a new and exciting research area of medical image processing. We are currently developing a nodule detection system. For the testing stage we have developed a method to insert simulated lung nodules into CT images. The simulated nodules can be used to produce corner cases to provide a better test environment for the segmentation technique than would be available through clinical data. The synthetic lung nodules produced by this program are based on a 2D Gaussian structure. This is modeled on the study of the structure of real lung nodules. We have also developed a lung segmentation technique, which is the first stage of our nodule detection system. The lungs are segmented using a combination of thresholding, morphology, 3D region growing, and volume analysis

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

[2]  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.

[3]  S. Armato,et al.  Computerized detection of pulmonary nodules on CT scans. , 1999, Radiographics : a review publication of the Radiological Society of North America, Inc.

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

[5]  Mohammad A. U. Khan,et al.  Lung nodule classification utilizing support vector machines , 2002, Proceedings. International Conference on Image Processing.

[6]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[7]  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.

[8]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.

[9]  Daw-Tung Lin,et al.  Lung nodules identification rules extraction with neural fuzzy network , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

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