Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thin-Plate Splines and the Atlas Problem for Biomedical Images
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
Automatic Retrieval of Anatomical Structures in 3D Medical Images
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Using a deformable surface model to obtain a shape representation of the cortex
ISCV '95 Proceedings of the International Symposium on Computer Vision
Finding 3D Parametric Representations of the Deep Cortical Folds
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Biomechanical Model Construction from Different Modalities: Application to Cardiac Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Registration of brain MR images using feature information of structural elements
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Variational image registration with local properties
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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A key issue in several brain imaging applications, including computer aided neurosurgery, functional image analysis, and morphometrics, is the spatial normalization and registration of tomographic images from different subjects. This paper proposes a technique for spatial normalization of brain images based on elastically deformable models. In our approach we use a deformable surface algorithm to find a parametric representation of the outer cortical surface and then use this representation to obtain a map between corresponding regions of the outer cortex in two different images. Based on the resulting map we then derive a three-dimensional elastic warping transformation which brings two images in register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, we use prestrained elasticity to model structural irregularities, and in particular the ventricular expansion occuring with aging or diseases. The performance of our algorithm is demonstrated on magnetic resonance images.