生物医学工程学杂志

生物医学工程学杂志

基于结构磁共振成像海马多特征组合的阿尔茨海默病分类新方法

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本文提出了一种利用结构磁共振图像多特征组合的阿尔茨海默病(AD)分类新方法。首先,利用 FreeSurfer 软件进行海马分割及皮层厚度、体积测量。然后,采用直方图、梯度、灰度共生矩阵及游程长度矩阵提取海马三维纹理特征,选取 AD、MCI 及 NC 三组间均具有显著差异的参数,与 MMSE 评分进行相关性研究。最后,利用极限学习机,对 AD、MCI 及 NC 进行分类识别。结果显示,无论左侧还是右侧,纹理特征相比于体积特征可以提供更好的分类结果;纹理、体积和皮层厚度互补的特征参量具有更高的分类识别率,且右侧(100%)分类正确率高于左侧(91.667%)。结果表明三维纹理分析可反映 AD 及 MCI 患者海马结构的病理变化,并且结合多特征的分析更能反映 AD 与 MCI 的认知障碍实质差别,更有利于临床鉴别诊断。

In this paper, a new method for the classification of Alzheimer’s disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.

关键词: 阿尔茨海默病; 海马; 三维纹理; 多特征组合; 极限学习机

Key words: Alzheimer’s disease; hippocampus; three-dimensional texture; multi-features combination; extreme learning machine

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