Image registration by local histogram matching
Image registration by local histogram matching
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DOI：
10.1016/j.patcog.2006.08.012
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发表时间：
2007-04-01
影响因子：
8
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
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作者：
Shen, Dinggang
我们以前提出了一种图像配准方法，称为分层属性匹配机制的弹性配准（HAMMER），这表明相对较高的精度在受试者间的MR脑图像配准。然而，HAMMER算法需要脑组织的预分割，因为用于分层匹配对应的点对的属性向量是从分割的图像定义的。在许多应用中，组织的分割可能是困难的、不可靠的或甚至不可能完成的，这潜在地限制了HAMMER算法在更广泛的应用中的使用。为了克服这一局限性，我们使用局部空间强度直方图来设计一种新的类型的属性向量的强度图像中的每个点。基于直方图的属性向量是旋转不变的，重要的是，它还通过整合来自原始强度图像的多分辨率图像的多个局部强度直方图来捕获空间信息。新的属性向量能够确定各个图像上的对应点。因此，通过分层匹配新的属性向量，所提出的方法可以成功地执行先前的HAMMER算法在注册MR脑图像，同时提供更广泛的应用在注册各种器官的图像。实验结果表明，该方法在MR脑图像配准中具有良好的性能。DTI脑图像、CT骨盆图像和MR小鼠图像。(c)2006模式识别学会。由爱思唯尔有限公司出版。保留所有权利。
We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images. DTI brain images, CT pelvis images, and MR mouse images. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.