The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D edge detection using recursive filtering: application to scanner images
CVGIP: Image Understanding
Active shape models—their training and application
Computer Vision and Image Understanding
Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
On digital distance transforms in three dimensions
Computer Vision and Image Understanding
A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes
Graphical Models and Image Processing
Robust Registration of Dissimilar Single and Multimodal Images
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
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A probabilistic deformable model for the representation of brain structures is described. The statistically learned deformable model represents the relative location of head (skull and scalp) and brain surfaces in Magnetic Resonance Images (MRIs) and accommodates their significant variability across different individuals. The head and brain surfaces of each volume are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the head and the brain is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loeve expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the probabilistic deformable model are presented: the deformable model-based registration of 3D multimodal (MR/SPECT) brain images without removing non-brain structures and the segmentation of the brain in MRI using the probabilistic constraints embedded in the deformable model. The multi-object deformable model may be considered as a first step towards the development of a general purpose probabilistic anatomical brain atlas.