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Unsupervised tissue type segmentation of 3D dual-echo MR head data

G. Gerig, J. Martin, R. Kikinis, O. Kübler, M. Shenton and F.A. Jolesz
Image and Vision computing
No. 6, pp. 349-360, July 1992

Abstract

The mental visualization of 3-D phenomena and the assessment of quantitative information from magnetic resonance (MR) image data require efficient semiautomated or automated segmentation techniques. The application of multivariate statistical classification to the segmentation of dual-echo volume data of the human head into anatomical objects and fluid spaces is studied in this paper. Tests of the radiometric variability of tissue classes within the data volume demonstrate the improvement of the image acquisition technology and the suitability of statistical methods to perform brain tissue segmentation. Supervised classification is successfully applied to a study of 16 MR volume images of the human head, illustrating the robustness of this method in segmenting brain (white and gray matter) and cerebrospinal fluid (csf). To omit subjective criteria involved in the supervised approach, ISODATA clustering as well as clustering based on nonparametric probability density estimation were tested. Both methods performed well (success rates 93.8\% and 87.5\% respectively), indicating that the classification procedure can be completely automated. The reproducibility and reliability of supervised and unsupervised classification were studied by comparing results of segmentation performed by five expert operators. Results suggest that the interoperator and intraoperator variations could be reduced using automated clustering techniques. The accuracy of the volume calculations was quantified by applying the MR imaging and segmentation process to a phantom resembling shape and tissue characteristics of brain tissues. The segmented brain objects are displayed using 3-D surface rendering.


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@Article{eth_biwi_00007,
  author = {G. Gerig and J. Martin and R. Kikinis and O. K\"ubler and M. Shenton and F.A. Jolesz},
  title = {Unsupervised tissue type segmentation of 3D dual-echo MR head data},
  journal = {Image and Vision computing},
  year = {1992},
  month = {July},
  pages = {349-360},
  volume = {},
  number = {6},
  keywords = {medical image analysis, classification, segmentation, anatomy, multispectral, MR imaging}
}