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Cluster analysis of diffusion tensor field with application to the segmentation of the Corpus Callosum

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posted on 26.09.2016, 11:09 authored by Safa Elsheikh, Andrew Fish, Roma Chakrabarti, Diwei ZhouDiwei Zhou
Accurate segmentation of the Corpus Callosum (CC) is an important aspect of clinical medicine and is used in the diagnosis of various brain disorders. In this paper, we propose an automated method for two and three dimensional segmentation of the CC using diffusion tensor imaging. It has been demonstrated that Hartigan’s K-means is more efficient than the traditional Lloyd algorithm for clustering. We adapt Hartigan’s K-means to be applicable for use with the metrics that have a f -mean (e.g. Cholesky, root Euclidean and log Euclidean). Then we applied the adapted Hartigan’s K-means, using Euclidean, Cholesky, root Euclidean and log Euclidean metrics along with Procrustes and Riemannian metrics (which need numerical solutions for mean computation), to diffusion tensor images of the brain to provide a segmentation of the CC. The log Euclidean and Riemannian metrics provide more accurate segmentations of the CC than the other metrics as they present the least variation of the shape and size of the tensors in the CC for 2D segmentation. They also yield a full shape of the splenium for the 3D segmentation.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

International Conference On Medical Imaging Understanding and Analysis

Citation

ELSHEIKH, S. ...et al., 2016. Cluster analysis of diffusion tensor field with application to the segmentation of the Corpus Callosum. Procedia Computer Science, 90, pp. 15–21.

Publisher

© The Authors. Published by Elsevier

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

29/04/2016

Publication date

2016-07-25

Notes

This paper was presented at the International Conference On Medical Imaging Understanding and Analysis (MIUA 2016), Loughborough, UK, 6-8th July. This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Attribution-NonCommercial-NoDerivatives Licence (CC BY-NC-ND). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/4.0/

ISSN

1877-0509

eISSN

1877-0509

Language

en

Location

Loughborough, UK