【作者】 林相波；

【导师】 邱天爽；

【作者基本信息】 大连理工大学 ， 生物医学工程， 2010， 博士

【摘要】 图像分割在医学图像分析中起着至关重要的作用。医学图像分割的目的是将一幅图像分成不重叠的多个区域,各区域对应人体明确的解剖结构。采用医学MRI图像,将人脑的不同器官分割出来并分析是临床诊断和辨识脑部疾病进程的重要手段。医学图像的复杂性使医学图像分割是一个非常复杂的任务,借助医学图谱提供的先验知识有助于更好的完成分割任务。配准技术是将医学先验知识融入分割过程的一个重要途径,形成了一种新的分割技术,即基于配准的分割。该类方法采用配准得到模板图像变形为目标图像的空间变换,然后利用这个空间变换将与模板图像相对应的已知分割结果,即图谱信息,映射到目标图像,从而得到对目标图像感兴趣区域的分割。所以,基于配准的分割质量主要取决于配准质量。人体结构的复杂性使得简单的刚性配准无法表示人体复杂结构的局部细节,而这些细节却是临床诊断的重要依据,因此有必要采用具有更高自由度的非刚性配准技术。同刚性配准相比,非刚性配准还不成熟,如何建立合理的形变模型,如何提高非刚性配准的速度、精度以及对非刚性配准的评估都需要进一步的研究。在目前的非刚性配准技术中,基于物理模型的非刚性配准以其特有的描述组织自然行为的能力,成为该领域中的一个重要分支。本文在深入研究基于热力学扩散模型的Demons非刚性配准技术的基础上,就灰度与空间标准化、变形场的正则化、先验知识对配准算法的性能影响等问题展开了研究,并提出了具有拓扑保持性的适用于单模态图像配准的混合灰度与特征信息的非刚性配准算法,然后将其应用到3D脑深层灰质结构分割中。具体成果介绍如下：1.分析、优化并实现了基于扩散模型的]Demons非刚性配准算法。基于物理模型的配准是一类重要的非刚性配准技术,其中Demons非刚性配准是该类方法中适用于单模态图像配准的一个优秀代表。本文对Demons算法及其重要的改进算法-主动Demons算法进行了深入细致的分析和算法实现,重点研究了该算法的两个重要参数,弹性系数和均化系数,及其形变驱动力的局限性,得出了实际应用时参数设置的一般性原则,并给出了改进的形变驱动力。2.提出了同时完成灰度与空间标准化的非刚性配准算法。以两图像灰度差的平方和作为衡量图像相似程度的非刚性配准算法完全依赖图像的灰度信息,其假设前提是两图像中对应像素点的灰度相同。然而,对于MRI图像,受不同扫描仪器、不同参数设置以及不均匀场、干扰、噪声等影响,难以满足这样的假设条件,需要进行灰度匹配预处理。另外,对于不同个体间的配准问题,不同个体的生理差异较大。过大的图像空间位置差异也会影响非刚性配准技术的配准质量,因此需要预先进行空间标准化处理。本文研究了非刚性配准的灰度和空间标准化问题,结合Demons算法,提出了一种分步配准策略,不仅能够完成灰度和空间的同步标准化,而且具有较高的描述局部微小形变的能力。3.提出了具有拓扑保持性的Demons非刚性配准算法。Demons算法的一个主要缺陷是不能从理论上保证配准得到的空间变换具有拓扑保持性。尽管该算法试图通过双射和变形场平滑技术得到具有拓扑不变性的空间变换,然而其可靠性未能得到理论证明和实验支持。该算法得到的是一个稠密的变形场,图像具有很高的变形自由度。高自由度意味着有更多的像素可以自由运动,如果没有附加约束,不能保证得到的空间变换是合理的,可以实现的。因此本文重点研究了变形场的约束问题,通过分析矢量场关键点特性,提出了在尽量保持原有变形场几何特性的前提下校正原变形场拓扑性的非刚性配准算法,并通过实验对算法性能进行了评价。4.提出了基于灰度与形状混合信息的非刚性配准算法。人脑的深层灰质结构,如尾状核、壳体、苍白球、丘脑、海马体等等,与帕金森氏综合征、癫痫、痴呆、克雅氏病等脑部疾病关系密切,通过分析这些结构的体积、形状变化等有助于相关的疾病诊断。然而这些结构形状复杂,体积小,在核磁共振图像中,它们的边界模糊,受部分容积效应的影响大。因此,深层脑结构的全自动分割是一个具有挑战性的问题。在已有的研究中,基于配准技术的分割是解决该问题一种有效手段。本文通过分析基于灰度的非刚性配准算法的基本原理和不足,在配准能量函数中引入形状相似度项,提出了新的灰度信息与形状信息相结合的非刚性配准算法。算法适用于相似灰度多目标分割问题,在分割体积小、形状复杂、边缘模糊的深层脑结构的应用中得到了较好的结果。更多还原

【Abstract】 Image segmentation plays an important role in medical image analysis. Medical image segmentation aims to divide an entire image into different non-overlapping regions. These regions represent different anatomical structures clearly. Segmenting and analyzing anatomical structures from brain MRI images is an effective method to diagnose brain diseases and track their evolvement. However the complexities of medical images make medical image segmentation a challenging task. Fortunately, the a priori knowledge provided by the atlas can contribute much to a better segmentation. Image registration is a good technique to fuse medical priori knowledge into segmentation procedure, which forms a new image segmentation technique, registration based segmentation. In registration based segmentation, an optimal spatial transformation deforming the source image into the target image is first obtained. Then the labeled structures in the atlas (correspondence to the source image) are mapped to the target image based on the obtained optimal spatial transformation. Finally, the required segmentation will be acquired easily. Obviously, the segmentation quality mainly depends on the registration quality.Using simple rigid registration to describe subtle local differences between individuals is insufficient due to the complexity of the human body structures. Therefore non-rigid registration techniques should be used to solve the problem. Non-rigid registration approaches usually have higher degree of freedom, and can describe more complex nonlinear deformations. Compared to the rigid registration, non-rigid registration is not mature. There are many problems to be solved, such as establishing a reasonable deformation model, improving the registration speed and accuracy, developing convincing algorithm evaluation criteria, and so on.Non-rigid registration based on physical models is an important branch in the current techniques, because they have unique abilities to describe the physical behaviors of organism. In this dissertation, we studied diffusion model based Demons non-rigid registration algorithm in depth, which originated from thermodynamic theory. The main researches focus on the intensity and spatial normalization, regularization method of the deformation field, the contributions of a priori knowledge to non-rigid registration and image segmentation. In segmenting deep brain internal structures applications, a hybrid intensity and shape knowledge non-rigid registration algorithm is proposed. The major contributions of this dissertation are as follows:1. Analyze, optimize, and implement diffusion model based Demons non-rigid registration algorithm.Demons non-rigid registration is one of excellent algorithms in physical model based registration. It is suitable to register two images within the same modality. In this dissertation, the Demons algorithm and its variant, active Demons algorithm, are studied in depth. Two important parameters, elasticity parameter and equalization parameter, and the limitations of the deformation force are analyzed carefully. Then instructive parameter setting principles and an improved deformation force are proposed.2. Propose a more localized non-rigid registration algorithm, which can complete spatial and intensity normalization simultaneously.Non-rigid registration algorithm using the sum of squared intensity differences as the similarity measure depends the image intensity knowledge absolutely. The assumption is that the intensities of two corresponding voxels are equal. However this condition is seldom fulfilled in real-world medical image registration without intensity normalization, because there are many factors that may affect observed intensities of a tissue over the imaged field, such as the different scanner or scanning parameters, normal aging, different subjects, and so on. In addition, there are large differences between individuals, which will influence the quality of inter-subject registration. Spatial normalization should also be done in advance. Combined with Demons algorithm, a step-wise non-rigid registration strategy is proposed. The proposed method not only can perform intensity and spatial normalization simultaneously, but also has higher localization ability.3. Propose a topology preserved Demons non-rigid registration algorithm.One disadvantage of Demons algorithm is that the topological invariance can not be ensured in theory, though bijectivity and deformation field smoothing technologies are adopted. The obtained deformation field is a dense vector field, which has a high deformation freedom. If there are no constraints to the deformation field, the spatial transformation can not be guaranteed reasonable and realizable. Through analyzing the critical points of a vector field, a topology preserved Demons non-rigid registration algorithm is proposed. In the context of preserving the characteristics as much as possible, the original deformation field changes to be topology preserved.4. Propose a hybrid intensity and shape features non-rigid registration algorithm.The deep brain internal structures, such as the caudate nucleus, putamen, thalamus, hippocampus, have close relationships with Parkinson’s syndrome, epilepsy, dementia, Creutzfeldt-Jakob and other brain diseases. Analyzing the size and shape changes will contribute to the clinical diagnosis. However, these structures have complex shapes, small sizes, fuzzy boundaries and large partial volume effect in MRI images. Therefore, segmenting deep brain internal structures is a challenging task. Among different segmentation methods, registration based segmentation method is promising. By analyzing basic principles and the weakness of the intensity based non-rigid registration algorithm, a new hybrid intensity and shape features non-rigid registration algorithm is proposed. It is suitable for multi-object segmentation problem. Better results are obtained when the proposed algorithm is used to segment deep brain internal structures.更多还原