Segmentation of striatal brain structures from high resolution PET images

  • Authors:

  • Ricardo J. P. C. Farinha;Ulla Ruotsalainen;Jussi Hirvonen;Lauri Tuominen;Jarmo Hietala;José M. Fonseca;Jussi Tohka

  • Affiliations:

  • Department of Signal Processing, Tampere University of Technology, Tampere, Finland and Department of Electrical Engineering, Faculty of Science and Technology, New University of Lisbon, Caparica, ...;Department of Signal Processing, Tampere University of Technology, Tampere, Finland;Department of Psychiatry, University of Turku, Turku, Finland and Turku PET Center, Neuropsychiatric Imaging, Turku University Central Hospital, Turku, Finland;Department of Psychiatry, University of Turku, Turku, Finland and Turku PET Center, Neuropsychiatric Imaging, Turku University Central Hospital, Turku, Finland;Department of Psychiatry, University of Turku, Turku, Finland and Turku PET Center, Neuropsychiatric Imaging, Turku University Central Hospital, Turku, Finland;Department of Electrical Engineering, Faculty of Science and Technology, New University of Lisbon, Caparica, Portugal;Department of Signal Processing, Tampere University of Technology, Tampere, Finland

  • Venue:
  • Journal of Biomedical Imaging
  • Year:
  • 2009

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Abstract

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric 11C-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.