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Artificial Intelligence
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SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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Journal of Computational Physics
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Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Volumetric shape description of range data using “Blobby Model”
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
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Proceedings of the 26th annual conference on Computer graphics and interactive techniques
SIAM Review
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Handbook of medical imaging
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SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
A Level-Set Approach for the Metamorphosis of Solid Models
IEEE Transactions on Visualization and Computer Graphics
The Morphological Structure of Images: The Differential Equations of Morphological Scale-Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Use of Active Shape Models for Locating Structures in Medical Images
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
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ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Level set surface editing operators
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Level set segmentation from multiple non-uniform volume datasets
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Segmentation of 3D Medical Structures Using Robust Ray Propagation
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CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
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This paper presents a framework for extracting surface models from a broad variety of volume datasets. These datasets are produced from standard 3D imaging devices, and are all noisy samplings of complex biological structures with boundaries that have low and often varying contrasts. The level set segmentation method, which is well documented in the literature, creates a new volume from the input data by solving an initial-value partial differential equation (PDE) with user-defined feature-extracting terms. However, level set deformations alone are not sufficient, they must be combined with powerful initialization techniques in order to produce successful segmentations. Our level set segmentation approach consists of defining a set of suitable pre-processing techniques for initialization and selecting/tuning different feature-extracting terms in the level set algorithm. This collection of techniques forms a toolkit that can be applied, under the guidance of a user, to segment a variety of volumetric data.