title: Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge.
abstract: Segmentation of the brain internal structures is an important and a challenging task due to their complex shapes, partial volume effects, low contrasts and anatomical variability between subjects. In this paper we propose a new non-rigid registration method that automatically segments the deep brain internal structures from brain MRI images. An atlas of the structures is used as a priori knowledge, which is modeled as a shape representation. By integrating the shape knowledge into a classical intensity based non-rigid registration algorithm, the proposed segmentation method allows to ameliorate the results in the case of low contrast on the boundaries of the structures. The shape model is based on distance representation obtained from the atlas. The segmentation of brain subcortical structures is performed on real MRI images and the obtained results are very encouraging.
authors.affiliation: CReSTIC, IUT de Troyes, 9 Rue de Québec, Troyes Cedex, France. linxbo@dlut.edu.cn
authors.forename: XiangBo
authors.initials: X
authors.lastname: Lin
authors.forename: Ruan
authors.initials: R
authors.lastname: Su
authors.forename: Frédéric
authors.initials: F
authors.lastname: Morain-Nicolier
authors.forename: TianShuang
authors.initials: T
authors.lastname: Qiu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D002536:Cerebral Arteries / Q000033:anatomy & histology*; D002552:Cerebral Ventricles / Q000033:anatomy & histology / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results