New approaches for measuring changes in the cortical surface using an automatic reconstruction algorithm

Dzung L. Pham, Xiao Han, Maryam E. Rettmann, Chenyang Xu, Duygu Tosun, Susan M. Resnick, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review


In previous work, the authors presented a multi-stage procedure for the semi-automatic reconstruction of the cerebral cortex from magnetic resonance images. This method suffered from several disadvantages. First, the tissue classification algorithm used can be sensitive to noise within the image. Second, manual interaction was required for masking out undesired regions of the brain image, such as the ventricles and putamen. Third, iterated median filters were used to perform a topology correction on the initial cortical surface, resulting in an overly smoothed initial surface. Finally, the deformable surface used to converge to the cortex had difficulty capturing narrow gyri. In this work, all four disadvantages of the procedure have been addressed. A more robust tissue classification algorithm is employed and the manual masking step is replaced by an automatic method involving level set deformable models. Instead of iterated median filters, an algorithm developed specifically for topology correction is used. The last disadvantage is addressed using an algorithm that artificially separates adjacent sulcal banks. The new procedure is more automated but also more accurate than the previous one. Its utility is demonstrated by performing a preliminary study on data from the Baltimore Longitudinal Study of Aging.

Original languageEnglish (US)
Pages (from-to)191-200
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4684 I
StatePublished - 2002


  • Cerebral cortex
  • Deformable models
  • Fuzzy clustering
  • Image segmentation
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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