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Authors: Olfa Ben Ahmed 1 ; Mohamed-Chacker Larabi 1 ; Marc Paccalin 2 and Christine Fernandez-Maloigne 1

Affiliations: 1 XLIM-SIC, UMR CNRS 7252, Futuroscope Chasseneuil Cedex, France ; 2 Université de Poitiers, Pôle de Gériatrie, France

Keyword(s): Alzheimer’s Disease, Saliency Maps, Visual Attention, Machine Learning, MRI, Domain Knowledge.

Abstract: Visual assessment of brain atrophy for brain diseases diagnosis by clinicians is the most widely adopted method in clinical practices. Such a visually extracted knowledge represents a great potential to develop better training programs and create new tools to assist clinical decision making. Inspired by the clinician visual behavior, we propose in this work a new and automatic approach to detect and quantify local brain atrophies. The proposed approach combines both bottom-up and top-down visual saliency using domain knowledge in the brain MRI analysis. The first subsystem relies on low-level MRI characterization (texture and edge) while the second is based on an embedded learning process to identify and localize the subset of gray matter regions that provides optimal discrimination between subjects. The proposed method validated for the task of Alzheimer’s disease (AD) subjects recognition. Classification experiments were conducted on a subset of 188 anatomical MR images ex tracted from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI) dataset. We report accuracy of 81.48% and 76.66% respectively for AD versus Normal Control (NC) and Mild Cognitive Impairment (MCI) versus NC classification tasks. (More)

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Paper citation in several formats:
Ben Ahmed, O.; Larabi, M.; Paccalin, M. and Fernandez-Maloigne, C. (2017). Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOIMAGING; ISBN 978-989-758-215-8; ISSN 2184-4305, SciTePress, pages 140-147. DOI: 10.5220/0006293001400147

@conference{bioimaging17,
author={Olfa {Ben Ahmed}. and Mohamed{-}Chacker Larabi. and Marc Paccalin. and Christine Fernandez{-}Maloigne.},
title={Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOIMAGING},
year={2017},
pages={140-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006293001400147},
isbn={978-989-758-215-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOIMAGING
TI - Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia
SN - 978-989-758-215-8
IS - 2184-4305
AU - Ben Ahmed, O.
AU - Larabi, M.
AU - Paccalin, M.
AU - Fernandez-Maloigne, C.
PY - 2017
SP - 140
EP - 147
DO - 10.5220/0006293001400147
PB - SciTePress

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