An adaptive mean-shift framework for MRI brain segmentation

Arnaldo Mayer, Hayit Greenspan

Research output: Contribution to journalArticlepeer-review

Abstract

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.

Original languageEnglish
Article number4781563
Pages (from-to)1238-1250
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume28
Issue number8
DOIs
StatePublished - Aug 2009

Keywords

  • Adaptive mean-shift
  • Brain magnetic resonance imaging (MRI)
  • Segmentation

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