基于多特征空间的均值漂移聚类算法及在fMRI中的应用

	基于多特征空间的均值漂移聚类算法及在fMRI中的应用
其他题名	Application of mean-shift clustering based onmulti-feature space to functional MRI
	张睿
学位类型	硕士
导师	高欣
	2015-10
学位授予单位	中国科学院大学
学位专业	机械工程
关键词	脑功能核磁共振成像均值漂移聚类频域特征邻域特征抗噪能力
摘要	功能磁共振成像(fMRI)能够无创获取脑活动图像数据，具有较高的空间和时间分辨率，是研究脑科学强有力的工具。然而，由于fMRI 信号变化幅度非常微弱，与噪声的波动几乎一致，使得从噪声中分离fMRI 信号，并进行脑激活区检测变得尤为困难。本论文针对现有脑激活区检测算法准确性低、可靠性差等问题，充分利用fMRI 数据的时间和空间特征，提出了两种新的激活区域检测算法。首先在fMRI 数据时间特征的基础上，提出了基于频域特征的均值漂移聚类算法(FD-MSC)。该方法将fMRI 频域信息与均值漂移聚类算法相结合，采用快速傅里叶变换获得fMRI 数据的频域特征空间，利用均值漂移算法对频域特征空间进行聚类搜索，获得激活区检测结果。其次在fMRI 数据空间特征的基础上，提出了基于邻域特征的均值漂移聚类算法(VN-MSC)。该方法将fMRI 空间邻域信息与均值漂移聚类算法相结合，采用互相关分析方法获得fMRI 数据的邻域特征空间，利用均值漂移算法对此邻域特征空间进行聚类搜索，完成对脑激活区域的检测。我们分别采用仿真数据和真实fMRI 数据对以上两种算法进行定量和定性评估，仿真实验结果表明，当选定合适的核宽，所提两种算法的敏感性和特异性均优于较传统的互相关分析算法(CCA)和互相关聚类算法(CCA+CA)。实际 fMRI 数据测试结果显示，所提两种算法同CCA 与CCA+CA 的结果具有良好的一致性，且所提算法的检测区域更完整。通过结果分析，FD-MSC 算法充分利用数据的时间特征，适合用于检测频域表现较强的fMRI 信号；VN-MSC 算法充分利用数据的空间特征，适合用于检测空间域高度相关的fMRI 信号。所提两种算法都具有良好的抗噪能力和高灵敏度，为fMRI 脑激活区域检测提供了一种新的方法。
其他摘要	Functional magnetic resonance imaging (fMRI) is a powerful tool for brain science. Due to the existence of noises, there is little difference between signal and noise in the region of interest, and it is difficult to extract fMRI signal directly. In this paper, we proposed two novel fMRI activation detection algorithms to overcome the disadvantages of current detection methods by utilizing the spatial features and temporal features. At first, on the basis of temporal features of fMRI data, we proposed a mean-shift clustering method based on frequency domain (FD-MSC) to detect the activated region in fMRI data. The proposed method combined temporal features with mean-shift clustering. The frequency domain was obtained by fast Fourier transform to construct a feature space, and then mean-shift clustering was adopted to detect the active region of fMRI based on the feature space. Second, on the basis of spatial features of fMRI data, we proposed a mean-shift clustering method based on voxel’s neighborhood (VN-MSC) for fMRI activated detection. The proposed method combined spatial features with mean-shift clustering. The voxel’s neighborhood obtained by cross-correlation analysis method was used to construct two dimensional feature spaces, which efficiently integrates fMRI data voxel’s neighborhood information. Finally, mean-shift clustering based on this feature space was adopted to detect active region of fMRI. The two proposed methods were applied to simulated data and real fMRI data for evaluation. The results of the evaluation using simulated data showed that the sensitivity and specificity of FD-MSC and VN-MSC with appropriate kernel size were better than cross-correlation analysis (CCA) and CCA plus cluster analysis (CCA+CA). The evaluation results of real fMRI data showed that the FD-MSC and VN-MSC method were consistent to other two methods (CCA, CCA+CA) in accuracy, while the detection region of the proposed methods was more complete. According to the evaluation results, the FD-MSC method makes full use of the temporal features of fMRI data and it is suitable for detection of the signal with stronger frequency domain. Meanwhile, the VN-MSC method makes full use of the spatial features of data and it is suitable for the detection of signal with stronger spatial correlation. The two proposed methods both have good robustness and high sensitivity, and they are suitable for fMRI activation analysis.
语种	中文
文献类型	学位论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/49310
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	张睿. 基于多特征空间的均值漂移聚类算法及在fMRI中的应用[D]. 中国科学院大学,2015.

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