title: Automatic cortical sulcal parcellation based on surface principal direction flow field tracking.
abstract: Automatic parcellation of cortical surfaces into sulcal based regions is of great importance in structural and functional mapping of human brain. In this paper, a novel method is proposed for automatic cortical sulcal parcellation based on the geometric characteristics of the cortical surface including its principal curvatures and principal directions. This method is composed of two major steps: 1) employing the hidden Markov random field model (HMRF) and the expectation maximization (EM) algorithm on the maximum principal curvatures of the cortical surface for sulcal region segmentation, and 2) using a principal direction flow field tracking method on the cortical surface for sulcal basin segmentation. The flow field is obtained by diffusing the principal direction field on the cortical surface. The method has been successfully applied to the inner cortical surfaces of twelve healthy human brain MR images. Both quantitative and qualitative evaluation results demonstrate the validity and efficiency of the proposed method.
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Jingxin
authors.initials: J
authors.lastname: Nie
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique