Interactive Shape Analysis of the Hippocampus in a Virtual Environment

가상 환경에서의 해마 모델에 대한 대화식 형상 분석☆

  • Published : 2009.10.30

Abstract

This paper presents an effective representation scheme for the shape analysis of the hippocampal structure and a stereoscopic-haptic environment to enhance sense of realism. The parametric model and the 3D skeleton represent various types of hippocampal shapes and they are stored in the Octree data structure. So they can be used for the interactive shape analysis. And the 3D skeleton-based pose normalization allows us to align a position and an orientation of the 3D hippocampal models constructed from multimodal medical imaging data. We also have trained Support Vector Machine (SVM) for classifying between the normal controls and epileptic patients. Results suggest that the presented representation scheme provides various level of shape representation and the SVM can be a useful classifier in analyzing the shape differences between two groups. A stereoscopic-haptic virtual environment combining an auto-stereoscopic display with a force-feedback (or haptic) device takes an advantage of 3D applications for medicine because it improves space and depth perception.

본 논문은 해마의 형상 분석을 위한 효과적인 모델 표현 방법과 분석 과정에서의 실제감을 향상시키는 스테레오-햅틱 장치 기반의 대화형 가상 환경을 제공한다. 매개변수형 표면 모델과 골격 표현은 해마의 형상을 효과적으로 표현하고 이러한 정보를 옥트리 자료 구조에 저장하여 대화형의 형상 분석 작업을 가능하게 한다. 그리고 골격 기반 정규화 방법은 다양한 모달리티를 갖는 의료 영상으로부터 생성된 3차원 해마 모델들의 위치와 방위를 정확하게 맞추어주는 기능을 수행한다. 또한 본 논문에서는 정상인 해마 형상 집단과 간질 환자 해마 형상 집단의 정확한 분류 작업을 수행하기 위하여 SVM 알고리즘 기반의 분류기 모델을 구축하였다. 실험 결과를 통하여 본 논문에서 제안한 표현 구조는 다양한 단계의 형상 표현을 제공하며 SVM 기반 분류기는 두 집단간 형상 차이를 분석하기 위한 효과적이었음을 확인하였다. 또한 스테레오 디스플레이 장치와 햅틱 장치를 결합한 가상환경은 사용자에게 향상된 공간 인지와 조작력을 제공하기 때문에 의료 분야에서의 해마 모델과 같은 다양한 해부학적 구조에 대한 분석 작업에 효과적으로 활용될 수 있다.

Keywords

References

  1. D. Dean, et al., Three dimensional MR-based morphometric comparison of schizophrenic and normal cerebral ventricles, Vis. In Biom. Computing, Lecture Notes in Comp. Sc., pp.363-372, 1996.
  2. C.R. Jack, MRI-based hippocampal volume measurements in epilepsy, Epilepsia, Vol. 35(Suppl. 6), pp. 21-29, 1994. https://doi.org/10.1111/j.1528-1157.1994.tb05986.x
  3. C.R. Jack, M.D. Bentley, C.K. Twomey, et al., MR imaging-based volume measurements of the hippocampal-formation and anterior temporal-lobe-validation studies, Radiology, Vol.176, No. 1, pp. 205-209, 1990. https://doi.org/10.1148/radiology.176.1.2353093
  4. S.M. Choi, M.H. Kim, Shape reconstruction from partially missing data in modal space, Computers & Graphics, Vol. 26, No. 5, pp. 701-708, 2002. https://doi.org/10.1016/S0097-8493(02)00125-5
  5. C. Brechbühler, G. Gerig, and O. Kübler, Parameterization of closed surfaces for 3-D shape description, Computer Vision, Graphics, Image Processing, Vol. 61, pp. 154-170, 1995.
  6. J.G. Csernansky, et al., Hippocampal deformities in schizophrenia characterized by high dimensional brain mapping, Am. J. Psychiatry, Vol. 159, pp. 1-7, 2002. https://doi.org/10.1176/appi.ajp.159.1.1
  7. T. Cootes, C.J. Taylor, D.H. Cooper, and J. Graham, Active shape models their training and application, Comp. Vis. Image Under., Vol. 61, pp. 38-59, 1995. https://doi.org/10.1006/cviu.1995.1004
  8. G. Gerig, M. Styner, D. Jones, D. Weinberger, and J. Lieberman, Shape analysis of brain ventricles using spharm, in MMBIA, IEEE Press, pp. 171-178, 2001.
  9. P. Golland, W.E.L. Grimson, and R. Kikinis, Statistical shape analysis using fixed topology skeletons: corpus callosum study, in IPMI, pp. 382-388, 1999.
  10. D. V. Vranic, 3D model retrieval, PhD thesis, University of Leipzig, 2004.
  11. .J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167, 1998. https://doi.org/10.1023/A:1009715923555
  12. O. Portillo-Rodriguez, et al., Haptic desktop: the virtual assistant designer, Proc. of the 2nd IEEE/ASME Interna-tional Conference, pp. 1-6, 2006.
  13. A.H. Mason, et al., Reaching movements to augmented and graphic objects in virtual environments, In Proc. of CHI ’01, pp. 426-433, 2001.
  14. M.A. Schnabel and T. Kvan, Spatial understanding in immersive virtual environments, Int. Journal of Architectural Computing (IJAC), Vol. 1, No. 4, pp. 435-448, 2003. https://doi.org/10.1260/147807703773633455
  15. S.A. Wall, et al., The effect of haptic feedback and stereo graphics in a 3D target acquisition task, Proc. of Eurohaptics, pp. 23-29, 2002.
  16. E.A. Karabassi, G. Papaioannou, and T. Theoharis, Afast depth-buffer-based voxelization algorithm, Journal of Graphics Tools, ACM, Vol. 4, No. 4, pp. 5-10, 1999. https://doi.org/10.1080/10867651.1999.10487510
  17. Z. Zhang, Iterative point matching for registration of freeform curves and surfaces, International Journal of Computer Vision, Vol. 13, No. 2, pp. 119-152, 1994. https://doi.org/10.1007/BF01427149
  18. T. F. Cootes, Statistical models of appearance for computer vision, 2004.
  19. C. Goodall, Procrustes methods in the statistical analysis of shape, Journal of the Royal Statistical Society B, Vol. 53, No. 2, pp. 285-339, 1991.
  20. B. Akka, Writing stereoscopic software for StereoGraphics systems using Hewlett Packard UNIX workstations, 1998.
  21. SeeReal Technologies, OpenGL 3D stereo programming guide for SeeReal 3D displays, 2004.
  22. B. Itkowitz, J. Handley and W. Zhu, The OpenHaptics Toolkit: a library for adding 3D touch navigation and haptics to graphics applications, Proc. of WHC05, pp. 657-667, 2005.