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Content Provider | IEEE Xplore Digital Library |
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Author | Aarabi, M.H. Kazerooni, A.F. Salehi, N. Rad, H.S. |
Copyright Year | 2014 |
Description | Author affiliation: Quantitative MR Imaging & Spectrosc. Group (QMISG), Tehran Univ. of Med. Sci., Tehran, Iran (Aarabi, M.H.; Kazerooni, A.F.; Salehi, N.; Rad, H.S.) |
Abstract | Diffusion tensor imaging (DTI) possesses high dimension and complex structure, so that detecting available pattern information and its analysis based on conventional linear statistics and classification methods become inefficient. In order to facilitate classification, segmentation, compression or visualization of the data, dimension reduction is far-reaching. There have been many approaches proposed for this purpose, which mostly rely on complex low dimensional manifold embedding of the high-dimensional space. Dimension reduction is commonly applicable through linear algorithms, such as principal component analysis and multi-dimensional scaling; however, they are not able to deal with complex and high dimensional data. In this light, nonlinear algorithms with the capability to preserve the distance of high dimensional data have been developed. The purpose of this paper is to propose a new method for meaningful visualization of brain white matter using diffusion tensor data to map the 6-dimensional tensor to a three dimensional space employing Markov random walk and diffusion distance algorithms, leading to a new distance-preserving map for the DTI data with lower dimension and higher throughput information. |
Sponsorship | IEEE Eng. Med. Biol. Soc. |
Starting Page | 2368 |
Ending Page | 2371 |
File Size | 890917 |
Page Count | 4 |
File Format | |
ISBN | 9781424479290 |
ISSN | 1557170X |
DOI | 10.1109/EMBC.2014.6944097 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-08-26 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Diffusion tensor imaging Manifolds Data visualization Tensile stress Biomedical imaging Principal component analysis diffusion tensor magnetic resonance imaging (DTMRI) human brain white matter nonlinear dimensionality reduction Diffusion Map distance-preserving mapping |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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