21 May 1999 Probabilistic multiobject deformable model for MR/SPECT brain image registration and segmentation
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Abstract
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 MR/SPECT images pairs and accommodates the significant variability of these anatomical structures across different individuals. To provide a training set, a representative collection of 3D MRI volumes of different patients have first been registered to a reference image. 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 MR image 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 variations modes of its Karhunen-Loeve expansion on the training population. By these means, both head and brain 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 and the segmentation of the brain from 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 atlas of the brain.
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Christophoros Nikou, Fabrice Heitz, Jean-Paul Armspach, "Probabilistic multiobject deformable model for MR/SPECT brain image registration and segmentation", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348571; https://doi.org/10.1117/12.348571
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