title: Topology preserving tissue classification with fast marching and topology templates.
abstract: This paper presents a novel approach for object segmentation in medical images that respects the topological relationships of multiple structures as given by a template. The algorithm combines advantages of tissue classification, digital topology, and level-set evolution into a topology-invariant multiple-object fast marching method. The technique can handle any given topology and enforces object-level relationships with little constraint over the geometry. Applied to brain segmentation, it sucessfully extracts gray matter and white matter structures with the correct spherical topology without topology correction or editing of the subcortical structures.
authors.affiliation: Laboratory of Medical Image Computing, Neuroradiology Division, Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287, USA.
authors.forename: Pierre-Louis
authors.initials: PL
authors.lastname: Bazin
authors.forename: Dzung L
authors.initials: DL
authors.lastname: Pham
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique