A minimum description length objective function for groupwise non-rigid image registration
A minimum description length objective function for groupwise non-rigid image registration
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DOI：
10.1016/j.imavis.2006.12.009
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发表时间：
2008-03-03
影响因子：
4.7
通讯作者：
Taylor, Chris J.
中科院分区：
文献类型：
--
作者：
Marsland, Stephen;Twining, Carole J.;Taylor, Chris J.
非刚性配准在一对图像之间找到密集的对应关系，使得两个图像中的类似结构对齐。虽然这对于图谱比较是足够的，但是为了使配准有助于诊断，需要在一组图像上执行配准。在本文中，我们描述了一个目标函数，可用于这组明智的注册。我们把图像配准问题看作是从一组样本图像中学习对应关系的问题之一（注册集），并推导出一个最小描述长度（MDL）的目标函数。我们给出了一个简单的描述MDL的方法，适用于传输两个单一的图像和图像集，并表明参考图像的概念（这对于在一组图像上定义一致的对应关系至关重要）自然地表现为MDL方法中的有效模型选择。我们使用我们的MDL目标函数在人脑的二维T1 MR图像上演示了刚性和非刚性分组配准，并表明我们获得了合理的对齐。对多模态情形的推广也进行了讨论。最后，我们讨论如何将MDL原则扩展到包括其他编码模型，而不是我们在这里提出的。(c)2007 Elsevier B.V.保留所有权利。
Non-rigid registration finds a dense correspondence between a pair of images, so that analogous structures in the two images are aligned. While this is sufficient for atlas comparisons, in order for registration to be an aid to diagnosis, registrations need to be performed on a set of images. In this paper, we describe an objective function that can be used for this group wise registration. We view the problem of image registration as one of learning correspondences from a set of exemplar images (the registration set), and derive a minimum description length (MDL) objective function.We give a brief description of the MDL approach as applied to transmitting both single images and sets of images, and show that the concept of a reference image (which is central to defining a consistent correspondence across a set of images) appears naturally as a valid model choice in the MDL approach.In this paper, we demonstrate both rigid and non-rigid groupwise registration using our MDL objective function on two-dimensional T1 MR images of the human brain, and show that we obtain a sensible alignment. The extension to the multi-modal case is also discussed. We conclude with a discussion as to how the MDL principle can be extended to include other encoding models than those we present here. (c) 2007 Elsevier B.V. All rights reserved.