The effect of the normalization strategy on voxel-based analysis of DTI images: A pattern recognition based assessment

Gloria Díaz*, Gonzalo Pajares, Eduardo Romero, Juan Alvarez-Linera, Eva López, Juan Antonio Hernández-Tamames, Norberto Malpica

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

3 Citations (Scopus)

Abstract

Quantitative analysis on diffusion tensor imaging (DTI) has shown be useful in the study of disease-related degeneration. More and more studies perform voxel-by-voxel comparisons of fractional anisotropy (FA) values, aiming at detecting white matter alterations. Overall, there is no agreement about how the normalization stage should be performed. The purpose of this study was to evaluate the effect of the normalization strategy on voxel-based analysis of DTI images, using the performance of a classification approach as objective measure of normalization quality. This is achieved by using a Support Vector Machine (SVM) which constructs a decision surface that allows binary classification with two types of regions, generated after a statistical evaluation of the grey level values of regions detected as statistically significant in a FA analysis.

Original languageEnglish
Title of host publicationBrain Informatics - International Conference, BI 2010, Proceedings
Pages78-88
Number of pages11
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 International Conference on Brain Informatics, BI 2010 - Toronto, ON, Canada
Duration: 28 Aug 201030 Aug 2010

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6334 LNAI
ISSN0302-9743

Conference

Conference2010 International Conference on Brain Informatics, BI 2010
Country/TerritoryCanada
CityToronto, ON
Period28/08/1030/08/10

Bibliographical note

Funding Information:
★Gloria Díaz is supported by a grant from the Colombian Department Technology and Innovation (COLCIENCIAS), Grant no. 109-2005.

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