ASSESSING THE VARIABILITY OF INTERNAL BRAIN STRUCTURES USING PCA ON SAMPLED SURFACE POINTS

Darwin Martínez, Isabelle Bloch, Tiberio Hernández

Abstract

In this paper we propose to analyze the variability of brain structures using principal component analysis (PCA). We rely on a data base of registered and segmented 3D MRI images of normal subjects. We propose to use as input of PCA sampled points on the surface of the considered objects, selected using uniformity criteria or based on mean and Gaussian curvatures. Results are shown on the lateral ventricles. The main variation tendencies are observed in the orthogonal eigenvector space. Dimensionality reduction can be achieved and the variability of each landmark point is accurately described using the first three components.

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Paper Citation


in Harvard Style

Martínez D., Bloch I. and Hernández T. (2009). ASSESSING THE VARIABILITY OF INTERNAL BRAIN STRUCTURES USING PCA ON SAMPLED SURFACE POINTS . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 172-179. DOI: 10.5220/0001790801720179


in Bibtex Style

@conference{visapp09,
author={Darwin Martínez and Isabelle Bloch and Tiberio Hernández},
title={ASSESSING THE VARIABILITY OF INTERNAL BRAIN STRUCTURES USING PCA ON SAMPLED SURFACE POINTS},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001790801720179},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - ASSESSING THE VARIABILITY OF INTERNAL BRAIN STRUCTURES USING PCA ON SAMPLED SURFACE POINTS
SN - 978-989-8111-69-2
AU - Martínez D.
AU - Bloch I.
AU - Hernández T.
PY - 2009
SP - 172
EP - 179
DO - 10.5220/0001790801720179