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Neuroimage. 2014 May 15;92:156-68. doi: 10.1016/j.neuroimage.2014.01.053. Epub 2014 Feb 9.

Automated segmentation and shape characterization of volumetric data.

Author information

1
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093-0407, USA. Electronic address: vit@ucsd.edu.
2
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Center for Functional MRI, University of California at San Diego, La Jolla, CA 92093-0677, USA. Electronic address: lfrank@ucsd.edu.

Abstract

Characterization of complex shapes embedded within volumetric data is an important step in a wide range of applications. Standard approaches to this problem employ surface-based methods that require inefficient, time consuming, and error prone steps of surface segmentation and inflation to satisfy the uniqueness or stability of subsequent surface fitting algorithms. Here we present a novel method based on a spherical wave decomposition (SWD) of the data that overcomes several of these limitations by directly analyzing the entire data volume, obviating the segmentation, inflation, and surface fitting steps, significantly reducing the computational time and eliminating topological errors while providing a more detailed quantitative description based upon a more complete theoretical framework of volumetric data. The method is demonstrated and compared to the current state-of-the-art neuroimaging methods for segmentation and characterization of volumetric magnetic resonance imaging data of the human brain.

KEYWORDS:

Morphometry; Segmentation; Spherical harmonics; Spherical wave decomposition

PMID:
24521852
PMCID:
PMC4324567
DOI:
10.1016/j.neuroimage.2014.01.053
[Indexed for MEDLINE]
Free PMC Article

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