Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm
Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm
复制标题
DOI：
10.1088/0031-9155/58/23/8359
复制
发表时间：
2013-12
影响因子：
3.5
通讯作者：
Xiaozheng Liu;Zhenming Yuan;Junming Zhu;Dongrong Xu
中科院分区：
文献类型：
--
作者：
Xiaozheng Liu;Zhenming Yuan;Junming Zhu;Dongrong Xu
Demons算法是一种计算效率高、实现简单的非刚性图像配准算法。经典的恶魔算法的变形力来自图像梯度，通过考虑变形来减少图像之间的强度差异。然而，利用图像灰度差进行医学图像配准的方法容易受到图像噪声、非均匀成像和部分容积效应等图像伪影的影响。梯度幅值图像是从图像的局部信息构造的，因此梯度幅值图像中的差异可以被认为对于这些伪影更可靠和鲁棒。然后，通过考虑图像强度和梯度幅度的差异来配准医学图像是一种直接的选择。本文在一种自同构Demons算法的基础上，提出了一种结合图像灰度和梯度幅值差异的链式自同构Demons算法，用于医学图像配准。以前的工作表明，经典的恶魔算法可以被认为是一个近似的二阶梯度下降的平方强度差的总和。通过优化新的相异度准则，我们还提出了一组新的恶魔力量，这是来自图像的梯度和梯度幅度图像。我们表明，在受控实验中，这种优势得到证实，并产生快速收敛。
The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence.