Cooperation between level set techniques and dense 3D registration for the segmentation of brain structures

Presents a cooperative strategy between volumetric registration and segmentation. The segmentation method is based on the level set formalism. Starting from an initial position, a closed 3D surface propagates towards the desired boundaries through the evolution of a 4D implicit function. We show that the number of iterations required for convergence is significantly reduced by using a registration process to initialize the surface. Furthermore it makes the segmentation fully automatic. The registration is achieved through a robust multiresolution and multigrid minimization scheme appropriate to our problem. In addition, a bidirectional propagation force depending on local intensity values has been designed for the evolution of the surface. Finally, an adaptive iteration step is automatically computed at each iteration in order to improve the robustness and the efficiency of the algorithm. Results on volumetric brain MR images are presented and discussed