导　　师： 李华

学科专业： 081203

授予学位： 博士

作　　者： ;

机构地区： 中国科学院研究生院

摘　　要： 生物医学成像仪器每天产生大量的图像数据。中国的数字人计划也已经获取了高分辨率、高质量的数据集。在这些来源不同的庞大数据中进行正常或病变组织、器官的定量分析与三维重建在科学研究和医学临床应用上非常重要。而图像分割是定量分析和产生高质量几何模型的前提。 一般把基于统计方法的图像分割称为组织分类。组织分类方法随图像模态、图像特征和图像质量不同而不同。本文针对神经、血管和脑组织这三种不同的数据,使用了三种不同的统计方法完成了组织分类。还针对大尺寸图像聚类提出了一种减少计算时间的种子点初始化方法。 本文提出了一种从臂丛神经扫描图像中提取神经微束并对神经微束进行自动分类的方法。扫描得到的彩色图像中,包含神经微束的神经束的外周边界很不清晰。通过神经微束质心的欧氏距离和神经微束最短距离的加权距离矩阵的凝结层次聚类算法能够对神经微束正确分类,通过误差平方和准则函数和上层类数先验知识可以自动判断神经束的数目。实验结果证明了这种方法的可行性。 本文提出了一种基于最大强度投影法预处理的有限混合模型方法提取磁共振血管造影图像中的血管。图像中强度较大的体素对应于血管或皮下脂肪,在分割时会干扰血管的提取。通过计算垂直于切片的Z-轴方向上的最大强度投影图像,二值化得到掩码图像。原始图像通过掩码图像处理后,用两个正态分布拟合,通过期望最大化方法计算混合模型的参数。用半高全宽和直方图曲线下的面积估计参数初值,减少了迭代计算次数,避免了收敛于局部极值点的缺点。正确地提取了血管并建立了几何模型。 本文提出用随机森林进行多通道图像的组织分类。通过自助重抽样技术产生多个训练样本,是用随机特征选择产生多个分类树,采用组合投票方法进行组织分类。结果表明,组合分类器是一种简单、快速、高效的多通道图像组织分类方法。 本文提出了一种对图像进行规律性二次抽样的方法,对生成的子样本进行聚类,产生的聚类中心作为原始图像聚类的初始中心。这种方法能够减少聚类中的迭代次数,加快计算速度。实验结果表明,规律性二次抽样是进行大尺寸图像聚类分析的有效方法。 本文最后总结并讨论了中国数字人项目下一步的研究工作。 Vast number of biomedical images are produced routinely everyday. Chinese Digital Human Project is an ongoing research plan in which two high-resolution and high-quality data sets including male and female have been acquired already. To the post-processing of the huge and diverse source of data sets, quantitative analysis and three dimensional reconstructions of normal or abnormal tissues and organs are essential. Image segmentation is the prerequisite of quantitative analysis and building high-quality geometric models. In general, the statistical based medical image segmentation method is also called statistical tissue classification. The tissue classification methods are different due to image modality, characteristics and quality etc. In this thesis, tissue classification is studied based on three different data sets: nerve, blood vessels and brain tissues. An improved K-means clustering algorithm was given which can be used to large size images. This dissertation proposed a method by which nerve micro bundles are extracted and classified automatically from the scanned images of the brachial plexus serial tissue sections. Weighted distance matrix based on the Euclidean distance of the centroid and the shortest distance of the nerve micro bundles was used in hierarchical clustering. A minimum squared error function was used to judge the number of clusters. Experiments showed that this classification method is feasible. This dissertation proposed a maximum intensity projection /(MIP/) preprocessing mask technique based finite mixture Gaussian model segmentation method for extracting blood vessels from brain magnetic resonance angiography /(MRA/) dataset. The voxels whose intensity is high in the dataset belong to blood vessels or subcutaneous fat, which may bias the adjustment of the blood vessels. MIP of the dataset in the Z axis direction was computed and segmented as a mask. The masked MRA dataset was segmented by a low threshold and the remanent voxels were modeled by two normal distributions. Expectation-Maximization /(EM/) algorithm was used to estimate the model parameters. The initial values of the parameters were setup close to the true value by full width half height /(FWHM/) and the area under the histogram curve, by which the computation iteration number is lessen, the computation is convergence and does not convergent to local maxima. The result showed that this method is feasible for vessel extraction from MRA dataset. This dissertation proposed random forest for segmentation of multi-channel images, which uses bootstrap resampling from the training samples to produce many sample set to train the same number of classification tree in which feature was randomly selected to construct random forests for multi-channel MR image segmentation. Experiments showed that this method has good segmentation performance.

关 键 词： 生物医学图像 加权距离矩阵 组合分类器 随机森林 期望最大化 自助法 最大强度投影法 混合模型 规律二次抽样 初始聚类中心

分 类 号： [TP391.41]

领　　域： [自动化与计算机技术] [自动化与计算机技术]