# Difference between revisions of "Projects:MultiscaleShapeSegmentation"

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## Revision as of 17:23, 21 August 2007

Home < Projects:MultiscaleShapeSegmentationBack to NA-MIC_Collaborations

## Contents

## 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)