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融合先验信息的脑功能区划分
Alternative TitlePrior information guided parcellation of the brain functional areas
宋丹丹
Subtype工学硕士
Thesis Advisor范勇
2013-05-29
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword核磁共振 功能连接 半监督聚类 先验信息 邻域信息 Fmri Functional Connectivity Semi-supervised Clustering Prior Information Spatial Proximity
Abstract基于功能磁共振成像(fMRI)的脑功能区划分就是采用聚类算法将脑区聚为功能不同的子区,通过对静息态功能连接的分析与处理来获取功能信号之间的相似性度量,将脑图像中功能相似的体素或区域聚为一类,从而实现脑功能子区的划分。已有的脑功能子区划分主要是根据功能信号的相关性采用无监督的聚类算法(K-means,层次聚类,归一化割算法等)进行脑功能区分割,而无监督的聚类算法往往用于没有可用的先验信息情况下,即脑区内体素的类别完全未知。本课题的研究目的是针对已有脑功能图像分割方法中存在的问题,采用融合先验信息的半监督聚类算法,用来确定脑功能子区间的可靠的边界,从而实现稳定的脑功能图像分割。主要包括以下两个内容: (1)本文提出了一种融合先验信息的半监督聚类算法对脑功能区进行划分,该方法有效地将前人的研究作为先验信息,利用脑区已经存在的细胞构筑、结构或功能等信息来确定部分可靠的分类初始点,这样在先验信息的指导下抑制噪声对分割边界的影响,从而获得更稳定的分割结果。基于106个正常被试的静息态fMRI数据的实验表明,我们成功地将扣带回分割为6个功能亚区,得到的结果不仅体现了个体差异性,而且还反映了子区内的功能同质性。为了验证该方法的鲁棒性,我们随机选取了多组初始标记点作为先验信息进行实验,而且为了验证分割结果的有效性和可靠性,我们分析了分割结果的功能连接模式以确认各子区之间是否存在功能差异性,另外我们也从meta分析的角度对结果进行了验证。 (2)为了消除分割结果中的孤立点得到平滑的分割结果,本文提出了一种改进的半监督聚类算法,该算法不仅融合了先验信息,而且还加入了空间邻域信息,通过算法的迭代,不仅把初始种子点的信息传递给与它相似性高的体素,而且把这些信息也传递给了与它空间邻近的体素,这样在先验信息和空间邻域信息的指导下共同抑制噪声对分割结果的影响,从而得到更光滑、更可信的分割结果。基于20个正常被试的静息态fMRI数据的实验表明,我们成功地将Broca区分为2个功能亚区,分割结果分别与较为公认的细胞构筑模板和无监督聚类算法得到的结果进行了比较,得出我们的算法比无监督聚类算法能获得更鲁棒、更可靠的分割结果。
Other AbstractBrain parcellation based on resting state fMRI data aims to automatically parcellate the brain regions into subunits with distinct functions using clustering algorithms. The brain parcellation is typically achieved by clustering brain image voxels according to a similarity measure defined by functional connectivity between functional signals. Most of the existing methods for brain parcellation adopt unsupervised clustering algorithms, e.g., K-means, hierarchical clustering, and normalized cut, which are not able to take into consideration any anantomical knowledge available. The present study has proposed a semi-supervised clustering method with a prior information to identify reliable boundaries between functional subunits for achieving a robust brain parcellation. The thesis mainly consists of following 2 parts. (1)A semi-supervised clustering method is proposed to achieve the brain parcellation guided by a prior information. The proposed method takes findings of the existing cytoarchitectonic studies, structural connectivity studies, and functional connectivity studies as a prior information to guide the brain parcellation for obtaining more robust parcellation results. Experiment results based on resting state fMRI data of 106 normal subjects have demonstrated that our method is able to successfully divide the cingulate cortex into six distinct functional subunits at an individual subject level. The robustness of our method has been validated using random initialization, and the validity and relibility of the pacellation results have been verified using functional connectivity pattern analysis and meta analysis. (2)An improved semi-supervised clustering algorithm is proposed to adopte spatial neighborhood information in the parcellation for obtaining smooth parcellation results. The proposed method iteratively propagates the label information of initial labeled voxels to their spatially nearest neighboring voxels with high similarity to them, facilitating smooth and reliable parcellation. Experiment resutls based on resting state fMRI data of 20 normal subjects have demonstrated that our method is able to divide the Broca area into two functionally distinct subunits. The comparison with established cytoarchitectonic data and parcellation results obtained by an unsupervised clustering algorithm has demonstrated our method is able to get more more accrute results.
shelfnumXWLW1906
Other Identifier201028014628050
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7660
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
宋丹丹. 融合先验信息的脑功能区划分[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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