A unified information theoretic framework for pair- and group-wise registration of medical images
A unified information theoretic framework for pair- and group-wise registration of medical images
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用于医学图像成对和成组配准的统一信息理论框架
DOI：
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发表时间：
2006
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通讯作者：
Lilla Zöllei
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作者：
Lilla Zöllei
在过去的二十年里，医学图像分析领域发展迅速。除了计算能力、扫描仪性能和存储设施的显著增长外，这种加速在一定程度上是由于研究人员可访问的数据集数量空前增加。医学专家传统上依赖于人工比较图像，但现在可用的丰富信息使这项任务变得越来越困难。这样的挑战促使人们在处理图像时实现更多的自动化。
为了在多个医学图像之间执行任何类型的比较，经常需要识别它们之间的适当对应关系。这一步使我们能够跟踪解剖学在一段时间间隔内发生的变化，识别个体之间的差异，或者从不同的数据模式中获取补充信息。注册实现了这样的对应。在本论文中，我们集中于统计配准方法的统一分析和刻画。
我们在一个统一的统计和信息理论框架下，制定和解释了一组精选的配对配准方法。这澄清了每种方法的隐含假设，并更好地了解了它们的相对优势和劣势。这引导我们找到一种新的配准算法，该算法结合了前面描述的方法的优点。接下来，我们通过对分组配准算法的分析来扩展统一的公式，该算法相对于成对的数据集来对齐总体。最后，我们给出了我们的分组注册框架--随机凝聚。该算法以同步的方式运行，种群中的每个成员都在同一调子上接近集合的中心趋势。它消除了先验地选择特定参考帧的需要，从而导致对数字模板的无偏估计。我们的算法采用信息论目标函数，该目标函数通过嵌入在多分辨率设置中的基于梯度的随机逼近过程进行优化。通过对合成图像和真实图像的实验，验证了随机凝聚算法的准确性和性能特点。(副本仅可从麻省理工学院图书馆获得。邮编：02139-4307.电话：617-253-5668；传真：617-253-1690。)
The field of medical image analysis has been rapidly growing for the past two decades. Besides a significant growth in computational power, scanner performance, and storage facilities, this acceleration is partially due to an unprecedented increase in the amount of data sets accessible for researchers. Medical experts traditionally rely on manual comparisons of images, but the abundance of information now available makes this task increasingly difficult. Such a challenge prompts for more automation in processing the images.
In order to carry out any sort of comparison among multiple medical images, one frequently needs to identify the proper correspondence between them. This step allows us to follow the changes that happen to anatomy throughout a time interval, to identify differences between individuals, or to acquire complementary information from different, data modalities. Registration achieves such a correspondence. In this dissertation we focus on the unified analysis and characterization of statistical registration approaches.
We formulate and interpret a select group of pair-wise registration methods in the context of a unified statistical and information theoretic framework. This clarifies the implicit assumptions of each method and yields a better understanding of their relative strengths and weaknesses. This guides us to a new registration algorithm that incorporates the advantages of the previously described methods. Next we extend the unified formulation with analysis of the group-wise registration algorithms that align a population as opposed to pairs of data sets. Finally, we present our group-wise registration framework, stochastic congealing. The algorithm runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same tune. It eliminates the need for selecting a particular reference frame a priori resulting in a non-biased estimate of a digital template. Our algorithm adopts an information theoretic objective function which is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We demonstrate the accuracy and performance characteristics of stochastic congealing via experiments on both synthetic and real images. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)