A novel layered data reduction mechanism for clustering fMRI data
A novel layered data reduction mechanism for clustering fMRI data
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DOI：
10.1016/j.bspc.2016.11.014
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Xiaoyan Tang;Weiming Zeng;Ni-zhuan Wang;Yuhu Shi;Le Zhao
中科院分区：
文献类型：
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作者：
Xiaoyan Tang;Weiming Zeng;Ni-zhuan Wang;Yuhu Shi;Le Zhao
原始的fMRI数据中往往含有由操作者、设备、环境等因素引起的各种噪声。为了抑制噪声，人们提出了许多基于平滑的处理方法来分析fMRI数据。在这项研究中，分层的数据减少机制，以减轻噪声的影响，同时保留的空间结构的功能磁共振成像数据。分层数据缩减方法包括两层准则来减少噪声体素：（a）孤立体素;（B）孤立生成立方体。1层数据简化过程旨在去除所有那些孤立的体素，其对应的生成的立方体仅包含一个单体素在预设阈值下。两层数据约简过程的目的是去除那些孤立的生成立方体，其对应的最终立方体只包含一个单一的生成立方体。提出了一种自适应确定最优阈值的简化遗传算法。同时，为了避免直接约简所有孤立体素和孤立生成立方体而丢失一些有用信息，利用多变量RV度量的补偿机制来降低不正确的数据约简概率。采用经典的FCM（模糊c均值）方法对分层数据约简方法实现的数据进行聚类。实验结果表明，该方法能够有效地提高混合数据和真实的fMRI数据的聚类精度。
Original fMRI data often contains a variety of noise caused by the operator, the equipment, the environment, etc. To suppress the noise, many processing methods based on smoothing have been proposed to analyze the fMRI data. In this study, a layered data reduction mechanism is presented to alleviate the influence of noise while retaining the spatial construction of the fMRI data. The layered data reduction method consists of two layers criteria to reduce the noise voxels:(a) the isolated voxel;(b) the isolated generated cube. The 1-layer data reduction procedure aims to remove all those isolated voxels whose corresponding generated cube only contains one single voxel under a preset threshold ξ. The 2-layer data reduction procedure is aimed at removing those isolated generated cubes whose corresponding final cube only contains one single generated cube. A simplified genetic algorithm (SGA) is proposed to determine the optimum threshold ξ adaptively. Meanwhile, to avoid that some useful information would be lost on account of all the isolated voxels and isolated generated cubes being reduced directly, a compensation mechanism taking advantage of multivariate RV measure is used to decrease the probability of incorrect data reduction. The classical FCM (Fuzzy c-means) method is adopted to cluster the data having been implemented by the layered data reduction method. Extensively experimental results show that the proposed layered data reduction method is effective and can efficiently improve the clustering accuracy on the hybrid data and the real fMRI data.