Abstract

Support Vector Machines (SVM) is a machine learning method used for classifying the system. It analyses and identifies the categories using the trained data. It is widely used in medical field for diagnosing the disease. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction gray level co- occurrence Matrix (GLCM) is been used. Extracted features are classified using different kernels of Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images.

References

• HoweMA, Gross BH. 1987 "CT evaluation of the equivocal pulmonary nodule", Computer Radiology, vol. 11, pp. 61–67
• H. Abe, H. MacMahon, J. Shiraishi, 2004 "Computer-aided diagnosis in chest radiology", Semin. Ultrasound CT MRI, Vol. 25, pp. 432-437.
• Kenji Suzuki, Hiroyuki Abe, Heber MacMahon, and Kunio Doi, 2006 "Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive Training Artificial Neural Network (MTANN)," IEEE Transactions on medical imaging, vol. 25, no. 4, pp. 406-416.
• Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T, 2001 "Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique", IEEE Trans. Med. Imaging, Vol. 20, pp. 595–604.
• Hyoungseop Kim, Seiji Mori, Yoshinori Itai, Seiji Ishikawa, Akiyoshi Yamamoto and Katsumi Nakamura, 2007" Automatic Detection of Ground-Glass Opacity Shadows by Three Characteristics on MDCT Images", World congress on medical physics and biomedical engineering, IFMBE Pro2 Vol. 14/4.
• L. Dougherty, J. C. Asmuth, and W. B. Gefter , 2003 "Alignment of CT lung volumes with an opticalflow method,"Acad. Radiol. , vol. 10, no. 3, pp. 249–254.
• Penedo, M. G. , Carreira, M. J. , Mosquera, A. and Cabello,D. ,1998 "Computer-Aided Diagnosis: A Neural-Network-Based Approach to Lung Nodule Detection", IEEE Transactions on Medical Imaging, Pp: 872 – 880.
• Okada K, Comaniciu D, Krishnan, 2005 "A Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CT", IEEE Trans. Med. Imaging, Vol. 24, No. 3, pp. 409–423.
• S. Hu, E. A. Hoffman, and J. M. Reinhardt , 2001 "Automatic lung segmen-tation for accurate quantitation of volumetric X-ray CT images,"IEEE Trans. Med. Imag. , vol. 20, no. 6, pp. 490–498.
• Ingrid Sluimer, Mathias Prokop, and Bram van Ginneken, 2005 " Toward Automated Segmentation of the Pathological Lung in CT", IEEE Transactions on Medical Imaging, vol. 24, no. 8.
• Yang Song, Weidong Cai, Jinman KimDavid Dagan Feng, 2012 " A Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images", IEEE transactions on Medical Imaging, vol. 31, no. 5.
• Xujiong Ye, Xinyu Lin, Jamshid Dehmeshki, Greg Slabaugh, and Gareth Beddoe, 2009 "Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images", IEEE Transactions on Biomedical Engineering, vol. 56, no. 7, pp. 1810-1820.
• S. Shaik Parveen, Dr. C. Kavitha, 2012 " A Review on Computer Aided Detection and Diagnosis of lung cancer nodules," International Journal of Computers & Technology, Volume 3 No. 3, Nov-Dec.
• Christopher J. C. Burges, 1998," A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery 2, 121-167.
• Parveen S. Shaik, Kavitha C, 2013, "Detection of lung cancer nodules using automatic region growing method", International Conference on Computing, Communications and Networking Technologies IEEE – ICCCNT Digital Object Identifier :10. 1109/ICCCNT. 2013. 6726669.