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基于磁共振影像的大脑皮层形状分析
其他题名MRI based shape analysis of the cerebral cortex
江界峰
学位类型工学硕士
导师蒋田仔
2009-05-27
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词磁共振成像 大脑皮层 形状分析 皮层厚度 球小波 分形维 计算神经解剖学 Magnetic Resonance Imaging Cerebral Cortex Shape Analysis Cortical Thickness Spherical Wavelets Fractal Dimension Computational Neuroanatomy
摘要人类大脑皮层的形状受到遗传、发育、可塑性、性别、精神神经疾病等多种因素影响。这导致了不同人群之间,以及人类个体间的大脑皮层存在着形状差异。通过分析大脑皮层形状,我们可以了解这些因素的作用机制,也可以辅助诊断精神神经疾病。结构磁共振影像的出现使我们能够更好地进行大脑皮层的形状分析。形状分析离不开各种各样的形状度量,本文介绍了在多个分辨率下,使用三种度量对大脑皮层进行形状分析的方法: 在局部层次,通过分析先天盲人和后天盲人的视觉皮层的厚度,我们研究了影响皮层厚度的主要因素,如突触发生和突触修剪等。我们发现先天盲人的双侧视觉皮层厚度明显大于后天盲人和正常志愿者,而后两组人的视觉皮层厚度没有显著差异。该发现可能反应了在发育关键期缺乏视觉经验所造成的视觉皮层突触修剪的减少。本文的结果支持了感觉经验是神经元的正常修剪和重塑过程的必要因素的假说,以及突触修剪可能是决定如皮层厚度等宏观解剖特征的主要因素的假说。 作为局部到整体的过渡层次,我们使用了球小波来压缩大脑皮层各点的空间坐标,使得能用少量的参数就能较为准确地描述大脑皮层的表面形状信息。基于该方法,我们提出了一个使用脑结构磁共振影像中重建的皮层表面来对精神分裂症患者进行分类的分类器。该方法在对男性精神分裂症患者的磁共振影像进行留一法验证时获得了较高的分类正确率。 在整体层次,本文提出了一种鲁棒及准确的分形维估计算法。该算法基于一种首次在脑研究中采用立方体-三角形相交检测的方法,以及在分形维估计中广泛采用的盒计数方法。这两种方法赋予了该算法鲁棒性。本算法的准确性通过人工生成的数据和真实磁共振影像得到了验证。
其他摘要The shape of human cerebral cortex is influenced by factors such as heredity, development, plasticity, gender, neuropsychiatric disorders and so on. As a result, variances exist in different populations and among individuals. By analyzing the shape of cerebral cortex, we could explore the mechanisms of the factors affecting the cerebral cortex, and help diagnose neuropsychiatric disorders. The emergence of structural magnetic resonance imaging (MRI) provides us a better technique to investigate the shape of cerebral cortex. The shape analysis depends on various measures. In this paper, we described how to perform shape analysis of the cerebral cortex via three different meansures in a multiresolution manner: At the local level, we investigated the key neurodevelopmental factors that determine cortical thickness, namely synaptogenesis and regression, by analyzing the thickness of the visual cortex in humans with early and late onset blindness. The bilateral visual cortices of the early blind were significantly thicker than those of the late blind and the sighted controls, but the latter two groups did not differ significantly. This suggests reduced “pruning” of synapses in the visual cortex, which may be due to a lack of visual experience during a critical developmental period. These findings support the hypothesis that sensory experience is necessary for an appropriate regression and remodeling of neuronal processes and that synaptic regression might be a major determinant of macroscopic anatomical features like cortical thickness. At the intermediate level, we employed spherical wavelets to encode the shape information of the cerebral cortex. Based on this method, we proposed a classifier discriminating schizophrenia patients using the surface shape features of the cerebral cortex reconstructed from MRI. Leave-One-Out validation achieved high accuracy in classifying male schizophrenia patients. At the global level, we proposed a robust and accuracy algorithm for fractal dimension estimation. This algotrithm based on a cubic-triangle intersection checking method which was used in brain research for the first time, and the widely used box-counting method in fractal dimension estimation. The two features endowed our algorithm robustness, and its accuracy was validated using both artificial and real MR images.
馆藏号XWLW1402
其他标识符200628014628035
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/7481
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
江界峰. 基于磁共振影像的大脑皮层形状分析[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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