IMPROVED SEGMENTATION OF MR BRAIN IMAGES INCLUDING BIAS FIELD CORRECTION BASED ON 3D-CSC

Haojun Wang, Patrick Sturm, Frank Schmitt, Lutz Priese

Abstract

The 3D Cell Structure Code (3D-CSC) is a fast region growing technique. However, directly adapted for segmentation of magnetic resonance (MR) brain images it has some limitations due to the variability of brain anatomical structure and the degradation of MR images by intensity inhomogeneities and noise. In this paper an improved approach is proposed. It starts with a preprocessing step which contains a 3D Kuwahara filter to reduce noise and a bias correction method to compensate intensity inhomogeneities. Next the 3D-CSC is applied, where a required similarity threshold is chosen automatically. In order to recognize gray and white matter, a histogram-based classification is applied. Morphological operations are used to break small bridges connecting gray value similar non-brain tissues with the gray matter. 8 real and 10 simulated T1-weighted MR images were evaluated to validate the performance of our method.

References

  1. Ashburner, J., Friston, K. J., 2000. Voxel-based morphometry-the methods. NeuroImage. 11(6Pt1): 805-821.
  2. Carlotto, M. J., 1987. Histogram analysis using a scalespace approach, IEEE Trans on PAMI. 9(1): 121-129.
  3. Duda. R. O., Hart, P. E., Stork, D. G., 2001. Pattern Classificatio, Wiley&SONS Press. London. 2nd edition.
  4. Kuwahara, M., Hachimura, K., Eiho, S., 1976. Processing of Ri-angiocardiographic images. Digital Processing of Biomedical Images. Plenum Press. New York.
  5. Liew, A. W., Yan, H., 2003. An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans on Medical Imaging. 22(9): 1063-1075.
  6. MacDonald, D., Kabani, N., Avis, D., 2000. Automated 3- D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage. 12(3): 340-356.
  7. Pham, D. L., Xu, C. Y., Prince, J. L., 2000. Current methods in medical image segmentation, Annual Review of Biomedical Engineering. 2: 315-337.
  8. Priese, L., Sturm, P., Wang, H. J., 2005. Hierarchical Cell Structures for Segmentation of Voxel Images. In SCIA2005, 14th Scandinavian Conference on Image Analysis. Springer Press.
  9. Rajapakse, J. C., Kruggel, F., 1998. Segmentation of MR images with intensity inhomogeneities. Image Vision Computing. 16(3): 165-180.
  10. Rehrmann, V., Priese, L., 1998. Fast and robust segmentation of natural color scenes. In ACCV98. 3rd Asian Conference on Computer Vision. Springer Press.
  11. Schnack, H. G., Hulshoff Pol, H. E., Baare, W. F., 2001. Automated separation of gray and white matter from MR images of the human brain, Neuroimage. 13(1): 230-237.
  12. Sled, J. G., Zijdenbos, A. P., Evans, A. C., 1998. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans on Medical Imaging. 17(1): 87-97.
  13. Stokking, R., Vincken, K. L., Viergever, M. A., 2000. Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 Data. NeuroImage. 12(6): 726-738.
  14. Sturm, P., 2004. 3D-Color-Structure-Code. A new nonplainness island hierarchy. In ICCSA 2004, International Conference on Computational Science and Its Applications. Springer Press.
  15. Suzuk, H., Toriwak, J., 1991. Automatic segmentation of head mri images by knowledge guided thresholding. Computerized Medical Imaging and Graphics. 15(4): 233-240.
  16. Vovk, U., Pernus, F., Likar, B., 2004. MRI intensity inhomogeneity correction by combining intensity and spatial information, Physics in Medicine and Biology. 49: 4119-4133.
  17. Wells III, W. M., Grimson, W. E. L., Kikinis, R. Jolesz, F. A. 1996. Adaptive segmentation of MRI data. IEEE Trans on Medical Imaging. 15(4): 429-442.
  18. Zhang, Y. Y., Brady, M., Smith, S., 2001. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans on Medical Imaging. 20(1): 45- 57.
Download


Paper Citation


in Harvard Style

Wang H., Sturm P., Schmitt F. and Priese L. (2006). IMPROVED SEGMENTATION OF MR BRAIN IMAGES INCLUDING BIAS FIELD CORRECTION BASED ON 3D-CSC . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 338-345. DOI: 10.5220/0001376503380345


in Bibtex Style

@conference{visapp06,
author={Haojun Wang and Patrick Sturm and Frank Schmitt and Lutz Priese},
title={IMPROVED SEGMENTATION OF MR BRAIN IMAGES INCLUDING BIAS FIELD CORRECTION BASED ON 3D-CSC},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={338-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001376503380345},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - IMPROVED SEGMENTATION OF MR BRAIN IMAGES INCLUDING BIAS FIELD CORRECTION BASED ON 3D-CSC
SN - 972-8865-40-6
AU - Wang H.
AU - Sturm P.
AU - Schmitt F.
AU - Priese L.
PY - 2006
SP - 338
EP - 345
DO - 10.5220/0001376503380345