3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA

Konstantin Levinski, Alexei Sourin, Vitali Zagorodnov

Abstract

Automatic segmentation of brain MRI data usually leaves some segmentation errors behind that are to be subsequently removed interactively using computer graphics tools. This interactive removal is normally performed by operating on individual 2D slices. It is very tedious and still leaves some segmentation errors which are not visible on the slices. We have proposed to perform a novel 3D interactive correction of brain segmentation errors introduced by the fully automatic segmentation algorithms. We have developed the tool which is based on a 3D semi-automatic propagation algorithm. The paper describes the implementation principles of the proposed tool and illustrates its application.

References

  1. Armstrong, C. J., B. L. Price, et al., 2007. Interactive segmentation of image volumes with Live Surface. Computers & Graphics 31 (2): 212-229.
  2. Bazin, P.-L., Pham, et al. 2005. Free software tools for atlas-based volumetric neuroimage analysis. Medical Imaging 2005: Image Processing 5747: 1824-1833.
  3. Boykov, Y. Y. and M. P. Jolly, 2001. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In International Conference on Computer Vision, ICCV 2001, 1: 105-112.
  4. de Bruin, P. W., V. J. Dercksen, et al., 2005. Interactive 3D segmentation using connected orthogonal contours. Computers in Biology and Medicine 35(4): 329-346.
  5. Falcao, A. X. and J. K. Udupa, 2000. A 3D generalization of user-steered live-wire segmentation. Medical Image Analysis 4 (4): 389-402.
  6. Giraldi, G., E. Strauss, et al., 2003. Dual-T-Snakes model for medical imaging segmentation. Pattern Recognition Letters 24(7): 993-1003.
  7. Hahn, H. K. and H.-O. Peitgen, 2003. IWT-interactive watershed transform: a hierarchical method for efficient interactive and automated segmentation of multidimensional gray-scale images. Medical Imaging 2003: Image Processing 5032: 643-653.
  8. Ibrahim, M., N. John, et al., 2006. Hidden Markov models-based 3D MRI brain segmentation. Image and Vision Computing 24(10): 1065-1079.
  9. Kang, Y., K. Engelke, et al. 2004. Interactive 3D editing tools for image segmentation. Medical Image Analysis 8(1): 35-46.
  10. Rohlfing, T. and J. C. R. Maurer 2005. Multi-classifier framework for atlas-based image segmentation. Pattern Recognition Letters 26(13): 2070-2079.
  11. Yushkevich, P. A., J. Piven, et al., 2006. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage 31(3): 1116-1128.
Download


Paper Citation


in Harvard Style

Levinski K., Sourin A. and Zagorodnov V. (2009). 3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA . In Proceedings of the Fourth International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2009) ISBN 978-989-8111-67-8, pages 111-118. DOI: 10.5220/0001657101110118


in Bibtex Style

@conference{grapp09,
author={Konstantin Levinski and Alexei Sourin and Vitali Zagorodnov},
title={3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA},
booktitle={Proceedings of the Fourth International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2009)},
year={2009},
pages={111-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001657101110118},
isbn={978-989-8111-67-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2009)
TI - 3D VISUALIZATION AND SEGMENTATION OF BRAIN MRI DATA
SN - 978-989-8111-67-8
AU - Levinski K.
AU - Sourin A.
AU - Zagorodnov V.
PY - 2009
SP - 111
EP - 118
DO - 10.5220/0001657101110118