한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권7호
  • /
  • Pages.994-1003
  • /
  • 2017
  • /
  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

흉부 CT 영상에서 다중 뷰 영상과 텍스처 분석을 통한 고형 성분이 작은 폐 간유리음영 결절 분류

Classification of Ground-Glass Opacity Nodules with Small Solid Components using Multiview Images and Texture Analysis in Chest CT Images

  • Lee, Seon Young (Dept. of Software Convergence, Seoul Women's University) ;
  • Jung, Julip (Dept. of Software Convergence, Seoul Women's University) ;
  • Lee, Han Sang (School of Electrical Engineering, KAIST) ;
  • Hong, Helen (Dept. of Software Convergence, Seoul Women's University)
  • 투고 : 2017.03.13
  • 심사 : 2017.06.21
  • 발행 : 2017.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

키워드

참고문헌

  1. C.I. Henschke, D.F. Yankelevitz, R. Mirtcheva, G. McGuinness, D. McCauley, and O.S. Miettinen, “CT Screening for Lung Cancer: Frequency and Significance of Partsolid and Nonsolid Nodules,” American Journal of Roentgenology, Vol. 178, No. 5, pp. 1053-1057, 2002. https://doi.org/10.2214/ajr.178.5.1781053
  2. K.H. Lee, J.M. Goo, S.J. Park, J.Y. Wi, D.H. Chung, H. Go, et al., “Correlation between the Size of the Solid Component on Thin-section CT and the Invasive Component on Pathology in Small Lung Adenocarcinomas Manifesting as Ground-glass Nodules,” Journal of Thoracic Oncology, Vol. 9, No. 1, pp. 74-82, 2014. https://doi.org/10.1097/JTO.0000000000000019
  3. J.H. Lee, C.M. Park, S.M. Lee, H. Kim, H.P. McAdams, and J.M. Goo, “Persistent Pulmonary Subsolid Nodules with Solid Portions of 5mm or Smaller: Their Natural Course and Predictors of Interval Growth,” European Radiology, Vol. 26, No. 6, pp. 1529-1537, 2015.
  4. T.N. Shewaye and A.A. Mekonnen, "Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features," arXiv Preprint arXiv:1605.08350, 2016.
  5. D. Kumar, A. Wong, and D.A. Clausi, "Lung Nodule Classification Using Deep Features in CT images," Proceeding of Computer and Robot Vision, pp. 133-138, 2015.
  6. S.K. Dilger, J. Uthoff, A. Judisch, E. Hammond, S.L. Mott, B.J. Smith, et al., "Improved Pulmonary Nodule Classification Utilizing Quantitative Lung Parenchyma Features," Journal of Medical Imaging, Vol. 2, No. 4, pp. 041004-041004, 2015. https://doi.org/10.1117/1.JMI.2.4.041004
  7. A. Kamiya, S. Murayama, H. Kamiya, T. Yamashiro, Y. Oshiro, and N. Tanaka, "Kurtosis and Skewness Assessments of Solid Lung Nodule Density Histograms: Differentiating Malignant from Benign Nodules on CT," Japanese Journal of Radiology, Vol. 32, No. 1, pp. 14-21, 2014. https://doi.org/10.1007/s11604-013-0264-y
  8. H.D. Chae, C.M. Park, S.J. Park, S.M. Lee, K.G. Kim, and J.M. Goo, “Computerized Texture Analysis of Persistent Part-solid ground-glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas,” Radiology, Vol. 273, No. 1, pp. 285-293, 2014. https://doi.org/10.1148/radiol.14132187
  9. S.A. Mattonen, D.A. Palma, C.J. Haasbeek, S. Senan, and A.D. Ward, "Early Prediction of Tumor Recurrence Based on CT Texture Changes after Stereotactic Ablative Radiotherapy (SABR) for Lung Cancer," Medical Physics, Vol. 41, No. 3, 033502, 2014. https://doi.org/10.1118/1.4866219
  10. J.Y. Son, H.Y. Lee, K.S. Lee, J.H. Kim, J. Han, J.Y. Jeong, et al., “Quantitative CT Analysis of Pulmonary Ground-glass Opacity Nodules for the Distinction of Invasive Adenocarcinoma from Pre-invasive or Minimally Invasive Adenocarcinoma,” PLoS One, Vol. 9, No. 8, pp. e104066, 2014. https://doi.org/10.1371/journal.pone.0104066
  11. J.Y. Son, H.Y. Lee, J.H. Kim, J. Han, J.Y. Jeong, K.S. Lee, et al, “Quantitative CT Analysis of Pulmonary Ground-glass Opacity Nodules for Distinguishing Invasive Adenocarcinoma from Non-invasive or Minimally Invasive Adenocarcinoma: the Added Value of Using Iodine Mapping,” European Radiology, Vol. 26, No. 1, pp. 43-54, 2016. https://doi.org/10.1007/s00330-015-3816-y
  12. S.Y. Lee, J. Jung, H.S. Lee, and H.Hong, "Pulmonary Ground-glass Opacity Nodules with Small Solid Portions Classification Using Texture Analysis in Chest CT Images," Proceeding of The 2016 Fall Conference of Korea Multimedia Soceity, pp. 234-236, 2016.
  13. J. Jung and H. Hong, “Automatic Segmentation of Ground-glass Opacity Nodule using Multi-phase Deformable Model with Intensity Constraint in Chest CT Images,” Journal of KI ISE : Software and Applications, Vol. 40, No. 10, pp. 582-591, 2013.
  14. R.M. Haralick and K. Shanmugam, “Texture Features for Image Classification,” IEEE Transactions on System, Man, and Cybernetics, Vol. 3, No. 6, pp. 610-621, 1973. https://doi.org/10.1109/TSMC.1973.4309314
  15. X. Tang, “Texture Information in Run-length Matrices,” IEEE Transactions on Image Processing, Vol. 7, No. 11, pp. 1602-1609, 1998. https://doi.org/10.1109/83.725367
  16. M. Robnik-Šikonja and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF,” Machine Learning, Vol. 53, No. 1-2, pp. 23-69, 2003. https://doi.org/10.1023/A:1025667309714
  17. C. Cortes and V. Vapnik, “Support-vector Networks,” Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995. https://doi.org/10.1007/BF00994018
  18. Y. Kim, J. Jung, H. Hong and S.I. Hwang, “Automated Prostate Cancer Detection on Multi-parametric MR imaging via Texture Analysis,” Journal of Korea Multimedia Society, Vol. 19, No. 4, pp. 736-746, 2016. https://doi.org/10.9717/kmms.2016.19.4.736