title: Segmentation of striatal brain structures from high resolution PET images.
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.
authors.affiliation: Department of Signal Processing, Tampere University of Technology, 33101 Tampere, Finland.
authors.forename: Ricardo J P C
authors.initials: RJ
authors.lastname: Farinha
authors.forename: Ulla
authors.initials: U
authors.lastname: Ruotsalainen
authors.forename: Jussi
authors.initials: J
authors.lastname: Hirvonen
authors.forename: Lauri
authors.initials: L
authors.lastname: Tuominen
authors.forename: Jarmo
authors.initials: J
authors.lastname: Hietala
authors.forename: José M
authors.initials: JM
authors.lastname: Fonseca
authors.forename: Jussi
authors.initials: J
authors.lastname: Tohka