한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

한국컴퓨터정보학회 (Korean Society of Computer Information)
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Region of Interest Heterogeneity Assessment for Image using Texture Analysis

  • Park, Yong Sung (Korea Institute of Radiological and Medical Sciences) ;
  • Kang, Joo Hyun (Korea Institute of Radiological and Medical Sciences) ;
  • Lim, Sang Moo (Korea Institute of Radiological and Medical Sciences) ;
  • Woo, Sang-Keun (Korea Institute of Radiological and Medical Sciences)
  • 투고 : 2016.08.29
  • 심사 : 2016.11.09
  • 발행 : 2016.11.30
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초록

Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

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참고문헌

  1. Bar-Shalom, R., Yefremov, N., Guralnik, L., Gaitini, D., Frenkel, A., Kuten, A., Altman, H., Keidar, Z., and Israel, O., "Clinical performance of PET/CT in evaluation of cancer: additional value for diagnostic imaging and patient management." Journal of nuclear medicine Vol. 44, No. 8, pp. 1200-1209, Aug. 2003.
  2. Burrell, R. A., McGranahan, N., Bartek, J., and Swanton, C., "The causes and consequences of genetic heterogeneity in cancer evolution." Nature, Vol. 501, No. 7467, pp. 338-345, July 2013. https://doi.org/10.1038/nature12625
  3. Burrell, R. A., and Swanton, C., "Tumour heterogeneity and the evolution of polyclonal drug resistance." Molecular oncology, Vol. 8, No. 6, pp. 1095-1111, Sep. 2014. https://doi.org/10.1016/j.molonc.2014.06.005
  4. Hyun, S.H., Kim, H.S., Choi, S.H., Choi, D.W., Lee, J.K., Lee, K.H., Park, J.O., Lee, K.H., Kim, B.T. and Choi, J.Y., "Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma." European journal of nuclear medicine and molecular imaging, pp. 1-8, Feb. 2016.
  5. Chicklore, S., Goh, V., Siddique, M., Roy, A., Marsden, P.K. and Cook, G.J., "Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis." European journal of nuclear medicine and molecular imaging, Vol. 40, No. 1, pp. 133-140, Jan. 2013. https://doi.org/10.1007/s00259-012-2247-0
  6. Haralick, R.M. and Shanmugam, K., "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics, Vol. 6, pp. 610-621, Nov. 1973.
  7. Loh, H.H., Leu, J.G. and Luo, R.C., "The analysis of natural textures using run length features." IEEE Transactions on Industrial Electronics, Vol. 35, No. 2, pp. 323-328, May 1988. https://doi.org/10.1109/41.192665
  8. Thibault, G., Fertil, B., Navarro, C., Pereira, S., Cau, P., Levy, N., Sequeira, J. and Mari, J.J., "Texture indexes and gray level size zone matrix application to cell nuclei classification." Pattern Recognition and Information Processing (PRIP), Minsk, Belarus, pp. 140-145, 2009.
  9. Fang, Y.H.D., Lin, C.Y., Shih, M.J., Wang, H.M., Ho, T.Y., Liao, C.T. and Yen, T.C., "Development and evaluation of an open-source software package "CGITA" for quantifying tumor heterogeneity with molecular images." BioMed research international, 2014, March 2014.
  10. Szczypinski, P.M., Strzelecki, M., Materka, A. and Klepaczko, A., "MaZda-A software package for image texture analysis." Computer methods and programs in biomedicine, Vol. 94, No. 1, pp. 66-76, April 2009. https://doi.org/10.1016/j.cmpb.2008.08.005
  11. Tesar, L., Shimizu, A., Smutek, D., Kobatake, H. and Nawano, S., "Medical image analysis of 3D CT images based on extension of Haralick texture features." Computerized Medical Imaging and Graphics, Vol. 32, No. 6, pp. 513-520, Sep. 2008. https://doi.org/10.1016/j.compmedimag.2008.05.005