대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

의료 두뇌영상의 익명성

Anonymity of Medical Brain Images

  • 이효종 (전북대학교 컴퓨터공학부, 영상정보신기술연구센터) ;
  • 두약유 (전북대학교 컴퓨터공학부)
  • Lee, Hyo-Jong (Div. of Computer Science and Engineering, CAIIT, Chonbuk National University) ;
  • Du, Ruoyu (Div. of Computer Science and Engineering, Chonbuk National University)
  • 투고 : 2011.07.06
  • 심사 : 2011.10.18
  • 발행 : 2012.01.25
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초록

현재 사용되고 있는 두뇌영상의 제거 방법은 비록 환자의 개인 정보를 보호하고 있으나, 과도한 제거로 정확한 두뇌영상의 무결성을 손실할 수 있다. 원래 두뇌의 영상과 동일한 두뇌 조직을 나타내면서 환자의 신원을 감출 수 있는 새로운 익명화 얼굴모델을 생성시키는 방법을 연구하였다. 제안방법은 두 단계로 구성되었다: 10명의 두뇌영상을 정규화시켜서 모조 두뇌 표본 영상을 생성하는 단계와 실험영상 두뇌의 외곽부를 모조 두뇌의 안면부로 대체시키는 단계이다. 전체 두뇌영상에서 두피와 두개골 영역을 분할하기 위하여 레벨셋 알고리즘을 적용하였다. 영역화된 모조 두뇌를 대상 두뇌영상에 동일하게 배치하고 정규화를 시켜서 익명화된 얼굴 모델을 생성하였다. 원래 영상과 변형된 영상의 두뇌 조직부의 밝기 변화를 비교하여 제안 알고리즘의 타당성을 실험하였다. 실험 결과 두 두뇌영상은 두뇌 조직에서 완전히 동일하면서 신원을 파악할 수 없는 것을 검증하였다.

The current defacing method for keeping an anonymity of brain images damages the integrity of a precise brain analysis due to over removal, although it maintains the patients' privacy. A novel method has been developed to create an anonymous face model while keeping the voxel values of an image exactly the same as that of the original one. The method contains two steps: construction of a mockup brain template from ten normalized brain images and a substitution of the mockup brain to the brain image. A level set segmentation algorithm is applied to segment a scalp-skull apart from the whole brain volume. The segmented mockup brain is coregistered and normalized to the subject brain image to create an anonymous face model. The validity of this modification is tested through comparing the intensity of voxels inside a brain area from the mockup brain with the original brain image. The result shows that the intensity of voxels inside from the mockup brain is same as ones from an original brain image, while its anonymity is guaranteed.

키워드

참고문헌

  1. R. Choplin, J. M. Boehme II and C. D. Maynard, "Picture archiving and communication system: an overview", RadioGraphics, vol. 12, no. 1, pp. 127-129, January 1992. https://doi.org/10.1148/radiographics.12.1.1734458
  2. Hipaa Program Office, "HIPAA Spotlights", http://hipaa.bsd.uchicago.edu/, 2011.
  3. National Electrical Manufacturers Association, "Digital Imaging and Communications in Medicine (DICOM)", PS 3-1, http://medical.nema. org/dicom/2004/04_01PU.PDF, 2004.
  4. Segonne F, Dale AM, Busa E, Glessner M, Salat D, Kahn HK, Fischl B. "A hybrid approach to the skull stripping problem in MRI," Neuroimage 2004;22:1060-1075. https://doi.org/10.1016/j.neuroimage.2004.03.032
  5. Sandor S, Leahy R. "Surface-based labeling of cortical anatomy using a deformable database," IEEE Trans Med Imaging 1997; 16:41-54. https://doi.org/10.1109/42.552054
  6. 이효종, "퍼지기반의 두뇌영상 영역분할 알고리듬", 전자공학회 논문지-TC편, 46권 12호, pp 1205-1210, 2009년 12월.
  7. A Bischoff-Grethe, B Fischl, B Ozyurt, S Morris, GG Brown, C Fennema-Notestine, CP Clark, MW Bondi, TL Jernigan, "A technique for the deidentification of structural brain MR images", Human Brain Mapping, Budapest, 2004 June.
  8. Fennema-Notestine C, Ozyurt IB, Brown GG, Clark CP, Morris S, Bischoff-Grethe A, Bondi MW, Jernigan TL, Fischl B, Segonne, "Quantitative evaluation of automated-skulltripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location," Hum Brain Mapp 27: 99-113, 2006. https://doi.org/10.1002/hbm.20161
  9. Mouse BIRN, http://www.loni.ucla.edu/BIRN/ Projects/Mouse/index.shtml, 2011.
  10. Osher, S., Sethian, J.A. "Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations", Journal of Computational Physics, 79:12-49, 1988 https://doi.org/10.1016/0021-9991(88)90002-2
  11. J.A. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press, pp382, 1996.
  12. R. Malladi, J. Sethian, and B. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Trans. Patt. Analysis and Mach. Intell., 17(2):158-175, February 1995. https://doi.org/10.1109/34.368173
  13. C. Baillard, P. Hellier, and C. Barillot. Segmentation of brain 3D MR images using level sets and dense registration. Medical Image Analysis Journal, 2000.
  14. Luis Ibaanez, Will Schroeder, Lydia Ng, Josh Cates, "The ITK Software Guide Second Edition-Updated for ITK version 2.4," The Insight Software Consortium, pp 836, 2005.