Automatic classification of sulcal regions of the human brain cortex using pattern recognition

Kirsten J. Behnke, Maryam E. Rettmann, Dzung L. Pham, Dinggang Shen, Susan M. Resnick, Christos Davatzikos, Jerry L. Prince

Research output: Contribution to journalConference articlepeer-review

24 Citations (Scopus)


Parcellation of the cortex has received a great deal of attention in magnetic resonance (MR) image analysis, but its usefulness has been limited by time-consuming algorithms that require manual labeling. An automatic labeling scheme is necessary to accurately and consistently parcellate a large number of brains. The large variation of cortical folding patterns makes automatic labeling a challenging problem, which cannot be solved by deformable atlas registration alone. In this work, an automated classification scheme that consists of a mix of both atlas driven and data driven methods is proposed to label the sulcal regions, which are defined as the gray matter regions of the cortical surface surrounding each sulcus. The premise for this algorithm is that sulcal regions can be classified according to the pattern of anatomical features (e.g. supramarginal gyrus, cuneus, etc.) associated with each region. Using a nearest-neighbor approach, a sulcal region is classified as being in the same class as the sulcus from a set of training data which has the nearest pattern of anatomical features. Using just one subject as training data, the algorithm correctly labeled 83% of the regions that make up the main sulci of the cortex.

Original languageEnglish
Pages (from-to)1499-1510
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5032 III
Publication statusPublished - 2003
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: 2003 Feb 172003 Feb 20


  • Atlas
  • Deformable models
  • Human brain cortex
  • Pattern recognition
  • Sulcal labeling
  • Sulci

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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