Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
A variational level set approach to multiphase motion
Journal of Computational Physics
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Brain segmentation with competitive level sets and fuzzy control
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Fuzzy shape-memory snakes for the automatic off-line signature verification problem
Fuzzy Sets and Systems
Journal of Biomedical Imaging
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
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We propose a new method to segment 3D structures with competitive level sets driven by a shape model and fuzzy control. To this end, several contours evolve simultaneously toward previously defined targets. The main contribution of this paper is the original introduction of prior information provided by a shape model, which is used as an anatomical atlas, into a fuzzy decision system. The shape information is combined with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the borders of their respective targets. The shape model is produced with a principal component analysis, and the resulting mean shape and variations are used to estimate the target location and the fuzzy states corresponding to the distance between the current contour and the target. By combining shape analysis and fuzzy control, we take advantage of both approaches to improve the level set segmentation process with prior information. Experiments are shown for the 3D segmentation of deep brain structures from MRI and a quantitative evaluation is performed on a 18 volumes dataset.