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Adaptive fusion of texture-based grading for Alzheimer's disease classification

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Adaptive fusion of texture-based grading for Alzheimer's disease classification

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dc.contributor.author Hett, Kilian es_ES
dc.contributor.author Ta, Vinh-Thong es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Coupe, Pierrick es_ES
dc.date.accessioned 2019-07-10T20:03:02Z
dc.date.available 2019-07-10T20:03:02Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0895-6111 es_ES
dc.identifier.uri http://hdl.handle.net/10251/123469
dc.description.abstract [EN] Alzheimer's disease is a neurodegenerative process leading to irreversible mental dysfunctions. To date, diagnosis is established after incurable brain structure alterations. The development of new biomarkers is crucial to perform an early detection of this disease. With the recent improvement of magnetic resonance imaging, numerous methods were proposed to improve computer-aided detection. Among these methods, patch-based grading framework demonstrated state-of-the-art performance. Usually, methods based on this framework use intensity or grey matter maps. However, it has been shown that texture filters improve classification performance in many cases. The aim of this work is to improve performance of patch-based grading framework with the development of a novel texture-based grading method. In this paper, we study the potential of multi-directional texture maps extracted with 3D Gabor filters to improve patch-based grading method. We also proposed a novel patch-based fusion scheme to efficiently combine multiple grading maps. To validate our approach, we study the optimal set of filters and compare the proposed method with different fusion schemes. In addition, we also compare our new texture-based grading biomarker with state-of-the-art methods. Experiments show an improvement of AD detection and prediction accuracy. Moreover, our method obtains competitive performance with 91.3% of accuracy and 94.6% of area under a curve for AD detection. (C) 2018 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (HL-MRI ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (BigDataBrain ANR-10-LABX-57). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computerized Medical Imaging and Graphics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Patch-based grading fusion es_ES
dc.subject Multi-features es_ES
dc.subject Alzheimer's disease classification es_ES
dc.subject Mild Cognitive Impairment es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Adaptive fusion of texture-based grading for Alzheimer's disease classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compmedimag.2018.08.002 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-IDEX-0003/FR/Initiative d’excellence de l’Université de Bordeaux/IDEX BORDEAUX/FR/Initiative d’excellence de l’Université de Bordeaux/IDEX BORDEAUX/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Hett, K.; Ta, V.; Manjón Herrera, JV.; Coupe, P. (2018). Adaptive fusion of texture-based grading for Alzheimer's disease classification. Computerized Medical Imaging and Graphics. 70:8-16. https://doi.org/10.1016/j.compmedimag.2018.08.002 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compmedimag.2018.08.002 es_ES
dc.description.upvformatpinicio 8 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 70 es_ES
dc.identifier.pmid 30273832
dc.relation.pasarela S\384865 es_ES
dc.contributor.funder Agence Nationale de la Recherche, Francia


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