title: Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI.
abstract: The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.
authors.affiliation: Department of Electrical and Electronic Engineering, Public University of Navarra, Pamplona, Spain. juanjose.cerrolaza@unavarra.es
authors.forename: Juan J
authors.initials: JJ
authors.lastname: Cerrolaza
authors.forename: Arantxa
authors.initials: A
authors.lastname: Villanueva
authors.forename: Rafael
authors.initials: R
authors.lastname: Cabeza
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D008959:Models, Neurological; D015233:Models, Statistical; D058067:Wavelet Analysis