AbstractWe present a new class of deformable models, MetaMorphs, that consist of both shape and interior texture. The model deformations are derived from both boundary and region information in a common variational framework. This framework represents a generalization of previous model-based segmentation approaches. The shapes of the new models are implicitly represented as an “image”, which is the higher dimensional embedding space of distance transforms. The interior textures are captured using a nonparametric kernelbased approximation of the intensity distribution inside the models. The models shapes can undergo both global and local deformations. The local deformations are efficiently parameterized using the cubic B-spline based Free Form Deformations (FFD). When using the models for boundary finding in images, we derive the model dynamics from an energy functional consisting of both edge energy terms and intensity/texture energy terms. This way, the models deform under the influence of forces derived from both boundary and regional information. The proposed MetaMorph deformable models are efficient in convergence, have large attraction range, and are robust to image noise and inhomogeities. Various examples on noisy medical images with complex textures demonstrate the potential of the proposed technique
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