Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia

Olfa Ben Ahmed, Mohamed-Chacker Larabi, Marc Paccalin, Christine Fernandez-Maloigne

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 extracted 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.

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Paper Citation


in Harvard Style

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 - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 140-147. DOI: 10.5220/0006293001400147


in Bibtex Style

@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 - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={140-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006293001400147},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - Saliency Guided Computer-aided Diagnosis for Neurodegenerative Dementia
SN - 978-989-758-215-8
AU - Ben Ahmed O.
AU - Larabi M.
AU - Paccalin M.
AU - Fernandez-Maloigne C.
PY - 2017
SP - 140
EP - 147
DO - 10.5220/0006293001400147