Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification.
This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims...
| Publicado en: | Journal of Alzheimer's Disease Vol. 65; no. 3; pp. 819 - 843 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131737573&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131737573 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872877 FLR jtl: Journal of Alzheimer's Disease issn: 13872877 maglogo: N pubinfo: dt: 2018 vid: 65 iid: 3 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 131737573 131737573 NLM29966190 131737573 10.3233/JAD-170514 NLM29966190 131737573 ppf: 819 ppct: 24 formats: tig: atl: Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification. aug: au: Ruiz, Elena Ramírez, Javier Górriz, Juan Manuel Casillas, Jorge Ramírez, Javier Górriz, Juan Manuel affil: Department of Computer Science and Artificial Intelligence sug: subj: Alzheimer's Disease Image Interpretation, Computer Assisted Methods Magnetic Resonance Imaging Methods Brain Prospective Studies Aged Body Weights and Measures Multivariate Analysis Sensitivity and Specificity Early Diagnosis Male Alzheimer's Disease Pathology Brain Pathology Human Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Aged: 65+ years Male Female ab: This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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