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DOI10.1142/S0218213012400222
AUTOMATED DETECTION OF MILD COGNITIVE IMPAIRMENT THROUGH MRI DATA ANALYSIS
Li, Lin2; Wang, James Z.1; Lozar, Carl3; Eckert, Mark A.3
通讯作者Wang, James Z.
来源期刊INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
ISSN0218-2130
EISSN1793-6349
出版年2012
卷号21期号:5
英文摘要

The early identification of mild cognitive impairment (MCI) has the potential for timely therapeutic interventions that would limit the advancement of MCI to Alzheimer’s disease (AD). This paper presents an automated approach for early detection of MCI through pattern classification of magnetic resonance imaging (MRI) data. The approach is based on image feature selection and support vector machine (SVM) classification. Subjects were selected from the Open Access Series of Imaging Studies (OASIS) database and included 89 MCI subjects and 80 controls. Voxel-by-voxel differences in gray matter (GM) intensity between the MCI and control groups were identified. Then regions of interest (ROIs) and the most discriminative image features that represented the patterns in MCI subjects were determined for training a classifier. The classifier demonstrated a high classification accuracy (90%) when a behavioral estimate of MCI and the ROIs were included as features in comparison to the behavioral estimate or the ROIs alone, which is one scientific contribution of our work. Another contribution is that the classifier can be integrated with the image processing functions through an online interface with significant medical capability that can be used for automated image pre-processing, obtaining MCI probability estimates for individual cases, and visualization of affected regions.


英文关键词Mild cognitive impairment pattern classification feature selection region of interest support vector machine computer-aided diagnosis
类型Article
语种英语
国家USA
收录类别SCI-E
WOS记录号WOS:000310637200004
WOS关键词VOXEL-BASED MORPHOMETRY ; PRODROMAL ALZHEIMERS-DISEASE ; TEMPORAL-LOBE ATROPHY ; GRAY-MATTER LOSS ; ENTORHINAL CORTEX ; BRAIN ATROPHY ; PATTERN-CLASSIFICATION ; MCI PATIENTS ; HIPPOCAMPUS ; BIOMARKERS
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS研究方向Computer Science
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/173023
作者单位1.Clemson Univ, Sch Comp, Clemson, SC 29634 USA;
2.Murray State Univ, Dept Comp Sci & Informat Syst, Murray, KY 42071 USA;
3.Med Univ S Carolina, Dept Otolaryngol Head & Neck Surg, Charleston, SC 29425 USA
推荐引用方式
GB/T 7714
Li, Lin,Wang, James Z.,Lozar, Carl,et al. AUTOMATED DETECTION OF MILD COGNITIVE IMPAIRMENT THROUGH MRI DATA ANALYSIS[J],2012,21(5).
APA Li, Lin,Wang, James Z.,Lozar, Carl,&Eckert, Mark A..(2012).AUTOMATED DETECTION OF MILD COGNITIVE IMPAIRMENT THROUGH MRI DATA ANALYSIS.INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS,21(5).
MLA Li, Lin,et al."AUTOMATED DETECTION OF MILD COGNITIVE IMPAIRMENT THROUGH MRI DATA ANALYSIS".INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 21.5(2012).
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