A non parametric mixed-effect model for population analysis: Application to Alzheimer's disease data

Juan David Ospina*, Oscar Acosta, Renaud De Crevoisier, Juan Carlos Correa, Pascal Haigron

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In population analysis, the images of different groups can be compared to locate the effects of a particular disease or treatment and also to generate biomarkers that help in the diagnosis process. Voxel-Based Morphometry (VBM) is a set of widely extended techniques to compare groups of images. VBM involves image normalization, image smoothing, statistical map generation and correction for hypothesis testing. In this paper, we propose the use of a nonparametric mixed-effect model to study Alzheimer's Disease (AD). The proposed method can handle covariates and through the integration of the smoothing and statistical map generation, individual specificities can be controlled. Moreover, it allows the reconstruction of the typical shapes for each group and it can be advantageously used as another VBM implementation.

Original languageEnglish
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages1124-1127
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: 2 May 20125 May 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period2/5/125/5/12

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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