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DOI | 10.1142/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
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ISSN | 0218-2130 |
EISSN | 1793-6349 |
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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