# Difference between revisions of "Projects:MultiscaleShapeSegmentation"

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= Description = | = Description = | ||

− | + | == 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 surfaceIn 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 surfaceIn 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. | ||

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''In Print'' | ''In Print'' | ||

− | * [http://www.na-mic.org/pages/ | + | * [http://www.na-mic.org/publications/pages/display?search=MultiscaleShapeSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database] |

[[Category:Shape Analysis]] [[Category:Segmentation]] [[Category:MRI]] [[Category:Schizophrenia]] | [[Category:Shape Analysis]] [[Category:Segmentation]] [[Category:MRI]] [[Category:Schizophrenia]] |

## Revision as of 19:18, 11 July 2009

Home < Projects:MultiscaleShapeSegmentationBack to NA-MIC Collaborations, Georgia Tech Algorithms, UNC Algorithms, GE Engineering, Kitware Engineering, Harvard DBP 1

# Multiscale Shape Segmentation

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.

# Description

## 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 surfaceIn 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.

# Key Investigators

- Georgia Tech Algorithms: Delphine Nain, Aaron Bobick, Allen Tannenbaum
- UNC Algorithms: Martin Styner
- GE Engineering: Jim Miller
- Kitware Engineering: Luis Ibanez
- Harvard DBP 1: Steven Haker, James Levitt, Marc Niethammer, Sylvain Bouix, Martha Shenton

# Publications

*In Print*