Cerebral magnetic resonance image segmentation using data fusion

Jagath C. Rajapakse, Charles DeCarli, Alan McLaughlin, Jay N. Giedd, Amy L. Krain, Susan D. Hamburger, Judith L. Rapoport

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Objective: A semiautomated method is described for segmenting dual echo MR head scans into gray and white matter and CSF. The method is applied to brain scans of 80 healthy children and adolescents. Materials and Methods: A probabilistic data fusion equation was used to combine simultaneously acquired T2-weighted and proton density head scans for tissue segmentation. The fusion equation optimizes the probability of a voxel being a particular tissue type, given the corresponding probabilities from both images. The algorithm accounts for the intensity inhomogeneities present in the images by fusion of local regions of the images. Results: The method was validated using a phantom (agarose gel with iron oxide particles) and hand-segmented images. Gray and white matter volumes for subjects aged 20-30 years were close to those previously published. White matter and CSF volume increased and gray matter volume decreased significantly across ages 4-18 years. White matter, gray matter, and CSF volumes were larger for males than for females. Males and females showed similar change of gray and white matter volumes with age. Conclusion: This simple, reliable, and valid method can be employed in clinical research for quantification of gray and white matter and CSF volumes in MR head scans. Increase in white matter volume may reflect ongoing axonal growth and myelination, and gray matter reductions may reflect synaptic pruning or cell death in the age span of 4-18 years.

Original languageEnglish (US)
Pages (from-to)206-218
Number of pages13
JournalJournal of Computer Assisted Tomography
Volume20
Issue number2
DOIs
StatePublished - 1996
Externally publishedYes

Keywords

  • Brain
  • Cerebrospinal fluid
  • Gray matter
  • Magnetic resonance imaging
  • Magnetic resonance imaging, physics and instrumentation
  • White matter

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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  • Cite this

    Rajapakse, J. C., DeCarli, C., McLaughlin, A., Giedd, J. N., Krain, A. L., Hamburger, S. D., & Rapoport, J. L. (1996). Cerebral magnetic resonance image segmentation using data fusion. Journal of Computer Assisted Tomography, 20(2), 206-218. https://doi.org/10.1097/00004728-199603000-00007