# Difference between revisions of "Projects:MultiscaleShapeSegmentation"

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+ | = Multiscale Shape Segmentation = | ||

+ | |||

Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]] | Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]] | ||

− | + | '''Objective''' | |

+ | |||

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. | To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. | ||

− | + | ''Overview'' | |

− | + | 'Shape Representation and Prior' | |

The overview of our shape representation is given in Figure 1. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population (Figure 2). We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations (Figure 4) [1]. | The overview of our shape representation is given in Figure 1. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population (Figure 2). We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations (Figure 4) [1]. | ||

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[[Image:Gatech_SW_mscale_shape.png|thumb|200px|Figure 2: A shape is represented using spherical wavelet coefficients]] | [[Image:Gatech_SW_mscale_shape.png|thumb|200px|Figure 2: A shape is represented using spherical wavelet coefficients]] | ||

− | + | ''Segmentation'' | |

Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. | Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. | ||

− | + | ''Results'' | |

We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia [2]. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details. | We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia [2]. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details. | ||

− | + | ''References'' | |

# Nain D, Haker S, Bobick A, Tannenbaum A. Multiscale 3D Shape Analysis using Spherical Wavelets. Proc MICCAI, Oct 26-29 2005, p 459-467. | # Nain D, Haker S, Bobick A, Tannenbaum A. Multiscale 3D Shape Analysis using Spherical Wavelets. Proc MICCAI, Oct 26-29 2005, p 459-467. | ||

# Nain D, Haker S, Bobick A, Tannenbaum A. Shape-driven 3D Segmentation using Spherical Wavelets. Proc MICCAI, Oct 2-5, 2006. | # Nain D, Haker S, Bobick A, Tannenbaum A. Shape-driven 3D Segmentation using Spherical Wavelets. Proc MICCAI, Oct 2-5, 2006. | ||

− | + | '''Key Investigators''' | |

+ | |||

* Georgia Tech: Delphine Nain, Aaron Bobick, Allen Tannenbaum | * Georgia Tech: Delphine Nain, Aaron Bobick, Allen Tannenbaum | ||

* Harvard SPL: Steven Haker | * Harvard SPL: Steven Haker | ||

− | + | ''Collaborators'' | |

* Core 1: Martin Styner (UNC) | * Core 1: Martin Styner (UNC) | ||

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* Core 3: James Levitt, Marc Niethammer, Sylvain Bouix, Martha Shenton (Harvard PNL) | * Core 3: James Levitt, Marc Niethammer, Sylvain Bouix, Martha Shenton (Harvard PNL) | ||

− | + | '''Links''' | |

+ | |||

* Paper presented in [[MICCAI_2006|MICCAI 2006, Copenhagen, October 2 - 4, 2006 ]] | * Paper presented in [[MICCAI_2006|MICCAI 2006, Copenhagen, October 2 - 4, 2006 ]] | ||

* Code: [[NA-MIC/Projects/Structural/Shape_Analysis/Spherical_Wavelets_in_ITK|ITK Spherical Wavelet Transform Filter]] | * Code: [[NA-MIC/Projects/Structural/Shape_Analysis/Spherical_Wavelets_in_ITK|ITK Spherical Wavelet Transform Filter]] |

## Revision as of 19:30, 3 September 2007

Home < Projects:MultiscaleShapeSegmentation# Multiscale Shape Segmentation

Back to NA-MIC_Collaborations

**Objective**

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus.

*Overview*

'Shape Representation and Prior' The overview of our shape representation is given in Figure 1. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population (Figure 2). We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations (Figure 4) [1].

*Segmentation*
Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner.

*Results*
We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia [2]. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

*References*

- Nain D, Haker S, Bobick A, Tannenbaum A. Multiscale 3D Shape Analysis using Spherical Wavelets. Proc MICCAI, Oct 26-29 2005, p 459-467.
- Nain D, Haker S, Bobick A, Tannenbaum A. Shape-driven 3D Segmentation using Spherical Wavelets. Proc MICCAI, Oct 2-5, 2006.

**Key Investigators**

- Georgia Tech: Delphine Nain, Aaron Bobick, Allen Tannenbaum
- Harvard SPL: Steven Haker

*Collaborators*

- Core 1: Martin Styner (UNC)
- Core 2: Jim Miller (GE), Luis Ibanez (Kitware)
- Core 3: James Levitt, Marc Niethammer, Sylvain Bouix, Martha Shenton (Harvard PNL)

**Links**