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

Huaizhong Zhang, Martin McGinnity, Sonya Coleman, Min Jing

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

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 have been 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.
Original languageEnglish
Title of host publicationNot Known
Publication statusAccepted/In press - 1 Jun 2011
Event22nd Irish Conference on Artificial Intelligence and Cognitive Science - Londonderry, United Kingdom
Duration: 31 Aug 20112 Sep 2011

Conference

Conference22nd Irish Conference on Artificial Intelligence and Cognitive Science
CountryUnited Kingdom
CityLondonderry
Period31/08/112/09/11

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 have been 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, M, Coleman, S & Jing, M 2011, Estimation of the Underlying Fiber Orientation Using Spherical k-means Method from the Diffusion ODF in HARDI Data. in Not Known. 22nd Irish Conference on Artificial Intelligence and Cognitive Science, Londonderry, United Kingdom, 31/08/11.

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

Not Known. 2011.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

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, Martin

AU - Coleman, Sonya

AU - Jing, Min

PY - 2011/6/1

Y1 - 2011/6/1

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 have been 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 have been 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 proceeding (ISBN)

BT - Not Known

ER -