Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent automated brain image segmentation
International Journal of Innovative Computing and Applications
Snake models for offline signature verification: a comparative study
International Journal of Innovative Computing and Applications
IEEE Transactions on Image Processing
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In this paper, a semi automatic method is proposed for the segmentation of mice cerebral structures (brain, cerebellum and hippocampus) in MR images. First, a Chan-Vese method is applied on the axial images to segment the brain volume. The method takes into account the special shape of the brain mice. Second, variational atlases are constructed by manual segmentation of various MRI brain images of reference and Trisomy 21 mice. These atlases are then registered on true data to assist the Chan-Vese segmentation of the cerebellum and the hippocampus. This semi automatic method makes that human intervention is limited and the tedious manual handling is greatly reduced. Results have shown that the brain volumes estimated by the method are identical to expert manually estimated volumes. The new method was used in the analysis of the cerebral malformations linked to Trisomy 21: no significant difference of the cerebral structures between Trisomy 21 mice and the control ones was found.