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CVGIP: Image Understanding
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Active blobs: region-based, deformable appearance models
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Bayesian approaches to motion-based image and video segmentation
IWCM'04 Proceedings of the 1st international conference on Complex motion
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
3D deformable registration for monitoring radiotherapy treatment in prostate cancer
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Using the local phase of the magnitude of the local structure tensor for image registration
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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This paper presents a new robust approach for registration and segmentation. Segmentation as well as registration is attained by morphing of an N-dimensional model, the Morphon, onto the Ndimensional data. The approach is general and can, in fact, be said to encompass much of the deformable model ideas that have evolved over the years. However, in contrast to commonly used models, a distinguishing feature of the Morphon approach is that it allows an intuitive interface for specifying prior information, hence the expression paint on priors. In this way it is simple to design Morphons for specific situations. The priors determine the behavior of the Morphon and can be seen as local data interpreters and response generators. There are three different kinds of priors: – material parameter fields (elasticity, viscosity, anisotropy etc.), – context fields (brightness, hue, scale, phase, anisotropy, certainty etc.) and – global programs (filter banks, estimation procedures, adaptive mechanisms etc.). The morphing is performed using a dense displacement field. The field is updated iteratively until a stop criterion is met. Both the material parameter and context fields are addressed via the present displacement field. In each iteration the neighborhood operators are applied, using both data and the displaced parameter fields, and an incremental displacement field is computed. An example of the performance is given using a 2D ultrasound heart image sequence where the purpose is to segment the heart wall. This is a difficult task even for trained specialists yet the Morphon generated segmentation is highly robust. Further it is demonstrated how the Morphon approach can be used to register the individual images. This is accomplished by first finding the displacement field that aligns the morphon model with the heart wall structure in each image separately and then using the displacement field differences to align the images.