A global optimisation method for robust affine registration of brain images
A global optimisation method for robust affine registration of brain images
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DOI：
10.1016/s1361-8415(01)00036-6
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发表时间：
2001-06-01
影响因子：
10.9
通讯作者：
Smith, S
中科院分区：
文献类型：
--
作者：
Jenkinson, M;Smith, S
配准是医学图像分析的重要组成部分，并且对于分析大量数据，期望具有全自动配准方法。到目前为止，已经提出了许多不同的自动配准方法，并且几乎所有方法都共享一个共同的数学框架-优化成本函数。到目前为止，很少有人关注优化方法本身，即使大多数配准方法的成功取决于这种优化的质量。本文探讨了假设潜在的问题，使用跨模态体素相似性措施的脑图像配准。它表明，使用本地优化方法与标准的多分辨率方法是不足以可靠地找到全局最小值。为了解决这个问题，提出了一种全局优化方法，专门针对这种形式的注册。包括所有必要的实施细节的充分讨论，因为这是任何实用方法的重要组成部分。此外，结果显示，所提出的方法是更可靠的，在寻找全球最小值比几个目前可用的注册包在共同使用的跨模态，跨学科的注册实验。(C)2001爱思唯尔科技有限公司。保留所有权利。
Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework - one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage. (C) 2001 Elsevier Science B.V. All rights reserved.