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3D Matrix Pattern Based Support Vector Machines for Identifying Pulmonary Cancer in CT Scanned Images

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Abstract

A novel algorithm of Three Dimension matrix (3D matrix) pattern based Minimum Within-Class Scatter Support Vector Machines (MCSVMs3Dmatrix) is presented. Combining Minimum Within-Class Scatter Support Vector Machines (MCSVMs) and higher-order tensor technology, decision functions of MCSVMs3Dmatrix are calculated along with three orthogonal directions in the 3D space. And then the final decision is made by Majority Vote Method. In previous reports, each CT image is solely processed and the relation among successive CT scanned images is neglected. The case results in defective judgment at whiles. The proposed method solves the problem effectively and improves the accuracy of classification to a certain extent.

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References

  1. Zhu, Y., Tan, Y., Hua, Y., et al., Feature selection and performance evaluation of support vector machine-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. J. Digit. Imaging 23(1):51–65, 2010.

    Article  Google Scholar 

  2. Yamamoto, S., Tanaka, I., Senda, M. et al., Image processing for computer-aided diagnosis of lung cancer by CT (LDCT). Systems and Computers in Japan. Syst. Comput. Jpn. 25(2):67–80, 1994.

    Google Scholar 

  3. Okumura, T., Miwa, T., Kako, J., et al., Image processing for computer-adided diagnosis of lung cancer screening system by CT(LDCT). Proc. SPIE. 3338:1314–1322, 1998.

    Article  Google Scholar 

  4. Rebelo, M. S., Furuie, S. S., Gutierrez, M. A., et al., Multiscale representation for automatic identification of structures in medical images. Comput. Biol. Med. 37(8):1183–1193, 2007.

    Article  Google Scholar 

  5. Ryan, W. J., Reed, J. E., Swensen, S. J. et al., Automatic detection of pulmonary nodules in CT. Proceedings: Computer Assisted Radiology. Amsterdam, the Netherlands: Elsevier Science. 385–389, 1996.

  6. Dehmeshki, J., Ye, X., and Valdivieso, M., Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imaging Graph. 31(6):408–417, 2007.

    Article  Google Scholar 

  7. Kanazawa, K., Kubo, M., Niki, N. et al., Computer assisted lung cancer diagnosis based on helical images. Image analysis applications and computer graphics: Lecture notes in computer science. SpringerLink. 1024:323–330, 1995.

  8. Jaffar, M. A., Hussain, A., and Mirza, A. M., Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images. Knowl. Inf. Syst. 7:1–21, 2009.

    Google Scholar 

  9. Giger, M. L., Bae, K. T., and MacMahon, H., Computerized detection of pulmonary nodules in computed tomography images. Invest. Radiol. 29:459–465, 1994.

    Article  Google Scholar 

  10. Armato, S. G., III, Giger, M. L., Moran, C. J., et al., Computerized detection of pulmonary nodules on CT scans. Radiographics 19:1303–1311, 1999.

    Google Scholar 

  11. Antonelli, M., Lazzerini, B., and Marcelloni, F., Segmentation and reconstruction of the lung volume in CT image. 20th annual ACM symposium on applied computing, vol I. Santa Fe, New Mexico, 5:255–299, 2000.

  12. Zhao, B., Gamsu, G., and Ginsberg, M. S., Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J. Appl. Clin. Med. Phys. 4(3):248–259, 2003.

    Article  Google Scholar 

  13. Joachims, T., Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of European Conference on Machine Learning (ECML), 1398:137–142, 1998.

  14. Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M., Haussler, D., Knowledge-based analysis of microarray gene expression data using support vector machines, 1999. http://www.cse.ucsc.edu/research/compbio/genex/genex.html. Santa Cruz, University of Califonia, Department of Computer Science and Engneering.

  15. Mika, S., Smola, A.J., and Scholkopf, B., An improved training algorithm for kernel fisher discriminants. Proceedings of the International Workshop on AI and Statistics (AISTATS). 98–104, 2001.

  16. Tefas, A., Kotropoulos, C., and Pitas, I., Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. PAMI 23(7):735–746, 2001.

    Article  Google Scholar 

  17. Zafeiriou, S. F., Tefas, A., and Pitas, I., Minimum class variance support vector machines. IEEE Trans. Image Process. 16(10):2551–2664, 2007.

    Article  MathSciNet  Google Scholar 

  18. Wang, Z., and Chen, S., New least squares support vector machines based on matrix patterns. Neural Process. Lett. 26:41–56, 2007.

    Article  Google Scholar 

  19. Gao, J., and Wang, S.-T., Matrix pattern based minimum within-class scatter support vector machines. Acta Electronica Sinica. 37(5):1051–1057, 2009.

    Google Scholar 

  20. Armato, S. G., III, Maryellen, L., et al., Computerized detection of pulmonary nodules on CT scans. Journal of continuing medical education in radiology. 19:1301–1311, 1999.

    Google Scholar 

  21. Williams, D. J., and Shah, M., A fast algorithm for active contours and curvature estimation. CVGIP Image Underst 55(1):14–26, 1992.

    Article  MATH  Google Scholar 

  22. De Lathauwer, L., De Moor, B., and Vandewalle, J., A multilinear singular value decomposition. SIAM J. Matrix Anal. 21(4):1253–1278, 2009.

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Communication Engineering, Jilin University, Changchun, China

    Qing-zhu Wang, Ke Wang & Xin-zhu Wang

  2. College of Computer Science and Engineering, Changchun University of Technology, Changchun, China

    A-lin Hou

  3. College of Information Engineering, Jilin Teachers’ Institute of Engineering & Technology, Changchun, China

    Yong Li

  4. Abdomal, Jilin Tumor Hospital, Changchun, China

    Bin Wang

Authors
  1. Qing-zhu Wang
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  2. Ke Wang
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  3. Xin-zhu Wang
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  4. A-lin Hou
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  5. Yong Li
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  6. Bin Wang
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Correspondence to Qing-zhu Wang.

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Wang, Qz., Wang, K., Wang, Xz. et al. 3D Matrix Pattern Based Support Vector Machines for Identifying Pulmonary Cancer in CT Scanned Images. J Med Syst 36, 1223–1228 (2012). https://doi.org/10.1007/s10916-010-9583-z

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  • DOI: https://doi.org/10.1007/s10916-010-9583-z

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