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Extraction and Tractography of Brain White Matter Region

Author: TongTong
Tutor: FengHuanQing;LiChuanFu
School: University of Science and Technology of China
Course: Biomedical Engineering
Keywords: MRI Diffusion tensor image White matter fiber tracking Dual tensor model Random walk algorithm
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 53
Quote: 0
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The human brain is one of the most complex systems in nature, the exploration of human cognitive processes is one of the most challenging problems in the current scientific research, and the development of modern brain imaging techniques for the study of the human brain provides a convenient non-invasive means. Early many studies are based on the EEG, MEG, and fMRI imaging technology, the human brain has a relatively different functions in different regions, namely \But the human brain to complete a simple task, but also always need a number of different functional areas of interaction and coordination, together constitute a network to complete instructions and tasks, namely \Because the human brain can be seen as a complex network, from the network's point of view it is very necessary to study the function of the human brain. Have emerged in recent years many articles about brain network building, from the network's point of view to study brain cognitive process to become a research focus on international. Brain network building process is the most critical step is the white matter fiber tracking, precise white matter fiber tracking is the basic premise of the brain network construction. Currently, there are a variety of white matter fiber tracking calculation method, but is divided into two main categories: decisive tracking and probabilistic tracking. Decisive tracking algorithm is based on a linear extension of thinking, to compute the fast but inaccurate calculation of the position of the fiber cross. The probabilistic tracking can solve the problem of the fiber cross, but its results are not intuitive and calculation time-consuming, and not suitable for clinical application. This article based on magnetic resonance imaging (MRI) brain image study of white matter regions the right track and on the basis of the implementation of fiber tracking method. Completed the following new research papers: 1. Both white matter tracking accuracy and speed of the algorithm based on the dual tensor model portfolio tracking algorithm. The dual tensor model can solve the fiber cross-cutting issues, which can improve the accuracy of the algorithm; decisive algorithm to compute the fast, making the proposed algorithm time complexity is reduced. In short, the algorithm to find the point of balance between the accuracy of the calculations and the calculation process rapidity, so that the white matter fiber tracking to be better able to be applied to clinical practice. 2. Prevent the algorithm when tracking white matter fiber tracts beyond the white matter regions, this paper improved Random Walks algorithm to extract the white matter regions form a binary mask and applied to the fiber tracking process, so tracking white matter fiber bundles restrictions In the white matter regions. Due to the complexity of the structure of the human brain, the original Random Walks can not be applied to the white matter extraction and achieve good segmentation results, in order to extract more accurate white matter regions, this paper introduces a local binary patterns and a priori probability model, thereby improving the segmentation accuracy of the white matter. 3 synthetic test data and real MRI data sets were used to verify the performance of the proposed algorithm. The experimental results show that the combination of tracking algorithm of this paper, not only a good solution to the fiber cross-cutting issues and can be effectively used in clinical studies. This thesis work was funded by the National Natural Science Foundation of China (60771007) and the Chinese Academy of Sciences, Graduate Innovation Fund.

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