title: Semi-automatic 3D segmentation of brain structures from MRI.
abstract: We present a semi-automatic 3D segmentation method for brain structures from Magnetic Resonance Imaging (MRI). There are three main contributions. First, our method combines boundary-based and region-based approaches but differs from previous hybrid methods in that we perform them in two separate phases. This allows for more efficient segmentation. Second, a probability map is generated and used throughout the segmentation to account for the brain structures with low-intensity contrast to the background. Third, we develop a set of tools for manual adjustment after the segmentation. This is particularly important in clinical research because the reliability of the results can be ensured. The experimental results and validations on different data sets are shown.
authors.affiliation: Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA. qhgb2@mizzou.edu
authors.forename: Qing
authors.initials: Q
authors.lastname: He
authors.forename: Kevin
authors.initials: K
authors.lastname: Karsch
authors.forename: Ye
authors.initials: Y
authors.lastname: Duan
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D002648:Child; D019295:Computational Biology; D057225:Data Mining; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D012984:Software; D014584:User-Computer Interface; D055815:Young Adult