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We focus on the images acquired using a novel brain dedicated high\u2010resolution (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<jats:italic>k<\/jats:italic>\u2010means 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\u2010resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development.<\/jats:p>","DOI":"10.1155\/2009\/156234","type":"journal-article","created":{"date-parts":[[2009,11,4]],"date-time":"2009-11-04T15:30:20Z","timestamp":1257348620000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Segmentation of Striatal Brain Structures from High Resolution PET Images"],"prefix":"10.1155","volume":"2009","author":[{"given":"Ricardo J. P. 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