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Updated: Jul 9, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Parsimonious model selection for tissue segmentation and classification applications: a study using simulated and

Raisa Z Freidlin1, Evren Ozarslan, Michal E Komlosh

  • 1Computational Bioscience and Engineering Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA.

IEEE Transactions on Medical Imaging
|November 29, 2007
PubMed
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This study explores using model selection criteria for diffusion tensor magnetic resonance imaging (dMRI) data analysis in fixed tissues. The Schwarz criterion (SC) offers an efficient and robust method for tissue segmentation and classification.

Area of Science:

  • Biomedical Imaging
  • Biophysics
  • Computational Biology

Background:

  • Diffusion tensor magnetic resonance (MR) imaging provides insights into tissue microstructure.
  • Accurate segmentation and classification of tissues in MR data are crucial for analysis.
  • Existing methods may be susceptible to artifacts from varying voxel properties.

Purpose of the Study:

  • To investigate the feasibility of using hierarchical models for diffusion tensor MR data in fixed tissues.
  • To assess the effectiveness of parsimonious model selection criteria for simultaneous tissue segmentation and classification.
  • To compare the performance of different model selection approaches.

Main Methods:

  • Utilized a hierarchy of models to describe diffusion tensor MR data.

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  • Employed parsimonious model selection criteria: Schwarz criterion (SC), F-test t-test (F-t), and F-test F-test (F-F).
  • Validated the framework using numerical phantoms and diffusion-weighted imaging (DWI) data from pig spinal cord.

    • Main Results:

    • The F-t approach showed robustness in distinguishing isotropic from general anisotropic models.
    • SC and F-F methods were preferred for segmenting models with transverse isotropy.
    • SC demonstrated superior performance, ease of implementation, and efficiency for large datasets.
    • The voxel-by-voxel segmentation framework is resilient to variance inhomogeneity artifacts.

    Conclusions:

    • The Schwarz criterion (SC) is an efficient and effective method for parsimonious model selection in diffusion tensor MR imaging.
    • This approach enables robust simultaneous tissue segmentation and classification, outperforming other methods.
    • The proposed framework is suitable for analyzing large MRI datasets and avoids common segmentation artifacts.