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通过最大化互信息进行多模态体积配准。

Multi-modal volume registration by maximization of mutual information.

作者信息

Wells W M, Viola P, Atsumi H, Nakajima S, Kikinis R

机构信息

Harvard Medical School, Department of Radiology, Boston, MA, USA.

出版信息

Med Image Anal. 1996 Mar;1(1):35-51. doi: 10.1016/s1361-8415(01)80004-9.

Abstract

A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with image data; no pre-processing or segmentation is required. This technique is, however, more flexible and robust than other intensity-based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, and with positron-emission tomography (PET) images. Surgical applications of the registration method are described.

摘要

提出了一种新的信息论方法来寻找不同模态的体积医学图像的配准。通过调整相对位置和方向,直到图像之间的互信息最大化来实现配准。在我们推导配准过程时,对成像过程的性质几乎没有做任何假设。因此,这些算法非常通用,可以预见可用于各种成像设备。这种方法直接处理图像数据;不需要预处理或分割。然而,与其他基于强度的技术(如相关性)相比,该技术更加灵活和稳健。此外,它有一个基于随机近似的高效实现。给出了实验,展示了该方法用于磁共振(MR)图像与计算机断层扫描(CT)图像以及正电子发射断层扫描(PET)图像的配准。描述了配准方法的手术应用。

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