Research Journal of Applied Sciences

Year: 2014
Volume: 9
Issue: 6
Page No. 299 - 307

Automatic Computer Aided Diagnosis System for Detection of Lung Cancer Nodules Using Region Growing Method and Support Vector Machines (SVM)

Authors : S. Shaik Parveen and C. Kavitha

References

Abe, H., H. Macmahon, J. Shiraishi, Q. Li, R. Engelmann and K. Doi, 2004. Computer-aided diagnosis in chest radiology. Seminar Ultrasound CT MR., 25: 432-437.
PubMed  |  

Bellotti, R., F. De Carlo, G. Gargano, S. Tangaro and D. Cascio et al., 2007. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Med. Phys., 34: 4901-4910.
PubMed  |  

Choi, W.J. and T.S. Choi, 2012. Genetic programming-based feature transform and classification for theautomatic detection of pulmonary nodules on computed tomography images. Inform. Sci., 212: 57-78.
CrossRef  |  

Dehmeshki, J., X. Ye, X. Lin, M. Valdivieso and H. Amin, 2007. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imaging Graoh., 31: 408-417.
CrossRef  |  PubMed  |  

Dougherty, L., J.C. Asmuth and W.B. Gefter, 2003. Alignment of CT lung volumes with an optical flow method. Acad. Radiol., 10: 249-254.
PubMed  |  

Ferrari, R.J., R.M. Rangayyan, J.E.L. Desautels and A.F. Frere, 2001. Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets. IEEE Trans. Med. Imaging, 20: 953-964.
PubMed  |  Direct Link  |  

Gomathi, M. and P. Thangaraj, 2010. A computer aided diagnosis system for lung cancer detection using support vector machine. Am. J. Applied Sci., 7: 1532-1538.

Howe, M.A. and B.H. Gross, 1987. CT evaluation of the equivocal pulmonary nodule. Comput. Radiol., 11: 61-67.
CrossRef  |  PubMed  |  

Hu, S., E.A. Hoffman and J.M. Reinhardt, 2001. Automatic lung segmentation for accurate quantization of volumetric X-ray CT images. IEEE Trans. Med. Imaging, 20: 490-498.
CrossRef  |  

Kim, H., T. Nakashima, Y. Itai, S. Maeda, J.K. Tan and S. Ishikawa, 2007. Automatic detection of ground glass opacity from the thoracic MDCT images by using density features. Proceedings of the International Conference on Control, Automation and Systems, October 17-20, 2007, Seoul, Korea, pp: 1274-1277.

Lee, Y., T. Hara, H. Fujita, S. Itoh and T. Ishigaki, 2001. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans. Med. Imaging, 20: 595-604.
PubMed  |  

Liu, C. and H. Wechsler, 2002. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Image Proc. IEEE Trans., 11: 467-476.
CrossRef  |  Direct Link  |  

Messay, T., R.C. Hardie and S.K. Rogers, 2010. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal., 14: 390-406.
CrossRef  |  

Okada, K., D. Comaniciu and A. Krishnan, 2005. Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT. IEEE Trans. Med. Imaging, 24: 409-423.
PubMed  |  

Parveen, S.S. and C. Kavitha, 2012. A review on computer aided detection and diagnosis of lung cancer nodules. Int. J. Comput. Technol., Vol. 3.

Penedo, M.G, M.J. Carreira., A. Mosquera and D. Cabello, 1998. Computer-aided diagnosis: A neural-network-based approach to lung nodule detection. IEEE Trans. Med. Imaging, 17: 872-880.
CrossRef  |  

Pohle, R. and K.D. Toennies, 2001. Segmentation of medical images using adaptive region growing. Proc. SPIE Medical Imaging, 4322: 1337-1346.
CrossRef  |  Direct Link  |  

Schiele, B. and J.L. Crowley, 2000. Recognition without correspondence using multidimensional receptive field histograms. Int. J. Comput. Vis., 36: 31-50.
CrossRef  |  Direct Link  |  

Shen, L., L. Bai and M. Fairhurst, 2007. Gabor wavelets and general discriminant analysis for face identification and verification. Image Vision Comput., 25: 553-563.
CrossRef  |  

Sluimer, I., M. Prokop and B. van Ginneken, 2005. Toward automated segmentation of the pathological lung in CT. IEEE Trans. Med. Imaging, 24: 1025-1038.
PubMed  |  

Song, Y., W. Cai, J. Kim and D.D. Feng, 2012. A multistage discriminative model for tumor and lymph node detection in thoracic images. IEEE Trans. Med. Imaging, 31: 1061-1075.
CrossRef  |  

Suarez-Cuenca, J.J., P.G. Tahoces, M. Souto, M.J. Lado, M. Remy-Jardin, J. Remy and J.J. Vidal, 2009. Application of the iris filter for automatic detection of pulmonary nodules on computed tomographyimages. Comput. Biol. Med., 39: 921-933.
CrossRef  |  

Suzuki, K., H. Abe, H. MacMahon and K. Doi, 2006. Image-processing technique for suppressing ribs in chest radiographs by means of Massive Training Artificial Neural Network (MTANN). IEEE Trans. Med. Imaging, 25: 406-416.
PubMed  |  Direct Link  |  

Ye, X., X. Lin, J. Dehmeshki, G. Slabaugh and G. Beddoe, 2009. Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng., 56: 1810-1820.
CrossRef  |  PubMed  |  

Yim, Y., H. Hong and Y.G. Shin, 2005. Hybrid lung segmentation in chest CT images for computer-aided diagnosis. Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, June 23-25, 2005, Busan, Korea, pp: 378-383.

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