3D Local Binary Pattern for PET Image Classification by SVM - Application to Early Alzheimer Disease Diagnosis

Christophe Montagne, Andreas Kodewitz, Vincent Vigneron, Virgile Giraud, Sylvie Lelandais

2013

Abstract

The early diagnostic of Alzheimer disease by non-invasive technique becomes a priority to improve the life of patient and his social environment by an adapted medical follow-up. This is a necessity facing the growing number of affected persons and the cost to our society caused by dementia. Computer based analysis of Fluorodeoxyglucose PET scans might become a possibility to make early diagnosis more efficient. Temporal and parietal lobes are the main location of medical findings. We have clues that in PET images these lobes contain more information about Alzheimer’s disease. We used a texture operator, the Local Binary Pattern, to include prior information about the localization of changes in the human brain. We use a Support Vector machine (SVM) to classify Alzheimer’s disease versus normal control group and to get better classification rates focusing on parietal and temporal lobes.

Paper Citation

in Harvard Style

Montagne C., Kodewitz A., Vigneron V., Giraud V. and Lelandais S. (2013). 3D Local Binary Pattern for PET Image Classification by SVM - Application to Early Alzheimer Disease Diagnosis . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 145-150. DOI: 10.5220/0004226201450150

in Bibtex Style

@conference{biosignals13,
author={Christophe Montagne and Andreas Kodewitz and Vincent Vigneron and Virgile Giraud and Sylvie Lelandais},
title={3D Local Binary Pattern for PET Image Classification by SVM - Application to Early Alzheimer Disease Diagnosis},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={145-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004226201450150},
isbn={978-989-8565-36-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - 3D Local Binary Pattern for PET Image Classification by SVM - Application to Early Alzheimer Disease Diagnosis
SN - 978-989-8565-36-5
AU - Montagne C.
AU - Kodewitz A.
AU - Vigneron V.
AU - Giraud V.
AU - Lelandais S.
PY - 2013
SP - 145
EP - 150
DO - 10.5220/0004226201450150