Display Settings:

Format

Send to:

Choose Destination
Int J Biomed Imaging. 2009;2009:156234. doi: 10.1155/2009/156234. Epub 2009 Nov 4.

Segmentation of striatal brain structures from high resolution PET images.

Author information

  • 1Department of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland.

Abstract

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric (11)C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structures from high-resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development.

PMID:
19911061
[PubMed]
PMCID:
PMC2773407
Free PMC Article

Images from this publication.See all images (6)Free text

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Algorithm 1
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for Hindawi Publishing Corporation Icon for PubMed Central
    Loading ...
    Write to the Help Desk