Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Incorporation of shape priors into geometric active contours
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
IEEE Transactions on Image Processing
A locally deformable statistical shape model
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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We present a model-driven approach to the segmentation of nasal cavity and paranasal sinus boundaries. Based on computed tomography data of a patients head, our approach aims to extract the border that separates the structures of interest from the rest of the head. This three-dimensional region information is useful in many clinical applications, e.g. diagnosis, surgical simulation, surgical planning and robot assisted surgery. The desired boundary can be made up of bone, mucosa or air what makes the segmentation process very difficult and brings traditional segmentation approaches, like e.g. region growing, to their limits. Motivated by the work of Tsai et al. [1] and Leventon et al. [2], we therefore show how a parametric level-set model can be generated from hand-segmented nasal cavity and paranasal sinus data that gives us the ability to transform the complex segmentation problem into a finited-imensional one. On this basis, we propose a processing chain for the automated segmentation of the endonasal structures that incorporates the model information and operates without any user interaction. Promising results are obtained by evaluating our approach on two-dimensional data slices of 50 patients with very diverse paranasal sinuses.