Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data

Huaizhong Zhang, TM McGinnity, SA Coleman, Min Jing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new approach for detecting the underlying fiber directions in a voxel. The main idea is to use the principal direction (centroid of orientation class) of an orientation population instead of the classical maximal direction of diffusion orientation density function (ODF) for fiber orientation. Firstly, diffusion orientations from the ODF of raw data are classified in accordance with the expected fiber populations. The centroids of diffusion orientations are then determined using the spherical k-means method so as to estimate fiber orientations. The proposed method is based on the reconstruction of diffusion ODF using spherical harmonic (SH) decomposition and the characterization of diffusion anisotropy in a voxel. It can approximate fiber orientations accurately and avoid the spurious detection of fiber orientation which is often observed with traditional methods. By using a variety of synthetic, phantom and real datasets, the experimental results demonstrate the effectiveness of the proposed method.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages96-105
Number of pages10
Publication statusPublished - 31 Aug 2011
EventIrish Conference on Artificial Intelligence and Cognitive Science - Londonderry
Duration: 31 Aug 2011 → …

Conference

ConferenceIrish Conference on Artificial Intelligence and Cognitive Science
Period31/08/11 → …

Fingerprint

Fiber reinforced materials
Probability density function
Fibers
Anisotropy
Decomposition

Cite this

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title = "Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data",
abstract = "This paper presents a new approach for detecting the underlying fiber directions in a voxel. The main idea is to use the principal direction (centroid of orientation class) of an orientation population instead of the classical maximal direction of diffusion orientation density function (ODF) for fiber orientation. Firstly, diffusion orientations from the ODF of raw data are classified in accordance with the expected fiber populations. The centroids of diffusion orientations are then determined using the spherical k-means method so as to estimate fiber orientations. The proposed method is based on the reconstruction of diffusion ODF using spherical harmonic (SH) decomposition and the characterization of diffusion anisotropy in a voxel. It can approximate fiber orientations accurately and avoid the spurious detection of fiber orientation which is often observed with traditional methods. By using a variety of synthetic, phantom and real datasets, the experimental results demonstrate the effectiveness of the proposed method.",
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Zhang, H, McGinnity, TM, Coleman, SA & Jing, M 2011, Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data. in Unknown Host Publication. pp. 96-105, Irish Conference on Artificial Intelligence and Cognitive Science, 31/08/11.

Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data. / Zhang, Huaizhong; McGinnity, TM; Coleman, SA; Jing, Min.

Unknown Host Publication. 2011. p. 96-105.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data

AU - Zhang, Huaizhong

AU - McGinnity, TM

AU - Coleman, SA

AU - Jing, Min

PY - 2011/8/31

Y1 - 2011/8/31

N2 - This paper presents a new approach for detecting the underlying fiber directions in a voxel. The main idea is to use the principal direction (centroid of orientation class) of an orientation population instead of the classical maximal direction of diffusion orientation density function (ODF) for fiber orientation. Firstly, diffusion orientations from the ODF of raw data are classified in accordance with the expected fiber populations. The centroids of diffusion orientations are then determined using the spherical k-means method so as to estimate fiber orientations. The proposed method is based on the reconstruction of diffusion ODF using spherical harmonic (SH) decomposition and the characterization of diffusion anisotropy in a voxel. It can approximate fiber orientations accurately and avoid the spurious detection of fiber orientation which is often observed with traditional methods. By using a variety of synthetic, phantom and real datasets, the experimental results demonstrate the effectiveness of the proposed method.

AB - This paper presents a new approach for detecting the underlying fiber directions in a voxel. The main idea is to use the principal direction (centroid of orientation class) of an orientation population instead of the classical maximal direction of diffusion orientation density function (ODF) for fiber orientation. Firstly, diffusion orientations from the ODF of raw data are classified in accordance with the expected fiber populations. The centroids of diffusion orientations are then determined using the spherical k-means method so as to estimate fiber orientations. The proposed method is based on the reconstruction of diffusion ODF using spherical harmonic (SH) decomposition and the characterization of diffusion anisotropy in a voxel. It can approximate fiber orientations accurately and avoid the spurious detection of fiber orientation which is often observed with traditional methods. By using a variety of synthetic, phantom and real datasets, the experimental results demonstrate the effectiveness of the proposed method.

M3 - Conference contribution

SP - 96

EP - 105

BT - Unknown Host Publication

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