Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets
Computer Vision and Image Understanding
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets
Computer Vision and Image Understanding
Ventricle boundary in CT: partial volume effect and local thresholds
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
Automatic landmarking of 2d medical shapes using the growing neural gas network
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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This paper presents a cooperative strategy for the segmentation of 3D brain MRI that integrates 3D segmentation and 3D registration methods. The segmentation is based on the level set formalism. A closed 3D surface representing the structure of interest iteratively propagates towards the desired boundaries through the evolution of a 4D implicit function. In this work, an adaptive propagation direction depending on local intensity values is used. In addition, an adaptive iteration step is automatically computed at each iteration in order to improve the robustness and the efficiency of the algorithm. The main contribution of this work is the use of an automatic registration method to initialize the surface, as an alternative solution to manual initialization. Registration is achieved through a robust multiresolution and multigrid minimization scheme. This cooperation significantly improves the quality of the method, since the segmentation is faster and fully automatic. Results on volumetric brain MR images are presented and discussed.