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International Journal of Biomedical Imaging
Volume 2009, Article ID 156234, 12 pages
Research Article

Segmentation of Striatal Brain Structures from High Resolution PET Images

1Department of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland
2Department of Electrical Engineering, Faculty of Science and Technology, New University of Lisbon, 2829-516 Caparica, Portugal
3Ramboll Finland Oy, 02241 Espoo, Finland
4Department of Psychiatry, University of Turku, 20700 Turku, Finland
5Turku PET Center, Neuropsychiatric Imaging, Turku University Central Hospital, 20520 Turku, Finland

Received 23 March 2009; Revised 10 June 2009; Accepted 11 August 2009

Academic Editor: J. C. Chen

Copyright © 2009 Ricardo J. P. C. Farinha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric -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.