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首页> 外文期刊>Medical image analysis >An information theoretic approach for non-rigid image registration using voxel class probabilities.
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An information theoretic approach for non-rigid image registration using voxel class probabilities.

机译：一种使用体素类概率进行非刚性图像配准的信息理论方法。

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摘要

We propose two information theoretic similarity measures that allow to incorporate tissue class information in non-rigid image registration. The first measure assumes that tissue class probabilities have been assigned to each of the images to be registered by prior segmentation of both of them. One image is then non-rigidly deformed to match the other such that the fuzzy overlap of corresponding voxel object labels becomes similar to the ideal case whereby the tissue probability maps of both images are identical. Image similarity is assessed during registration by the divergence between the ideal and actual joint class probability distributions of both images. A second registration measure is proposed that applies in case a segmentation is available for only one of the images, for instance an atlas image that is to be matched to a study image to guide the segmentation thereof. Intensities in one image are matched to the fuzzy class labels in the other image by minimizing the conditional entropy of the intensities in the first image given the class labels in the second image. We derive analytic expressions for the gradient of each measure with respect to individual voxel displacements to derive a force field that drives the registration process, which is regularized by a viscous fluid model. The performance of the class-based measures is evaluated in the context of non-rigid inter-subject registration and atlas-based segmentation of MR brain images and compared with maximization of mutual information using only intensity information. Our results demonstrate that incorporation of class information in the registration measure significantly improves the overlap between corresponding tissue classes after non-rigid matching. The methods proposed here open new perspectives for integrating segmentation and registration in a single process, whereby the output of one is used to guide the other.
机译：我们提出了两种信息理论相似性度量，这些度量允许将组织类信息纳入非刚性图像配准中。第一种措施假定已经通过对两个图像的先前分割将组织类别概率分配给要配准的每个图像。然后将一个图像非刚性变形以匹配另一个图像，以使相应体素对象标签的模糊重叠变得与理想情况相似，从而使两个图像的组织概率图相同。在配准过程中，图像的相似性是通过两个图像的理想和实际联合类别概率分布之间的差异来评估的。提出了第二配准措施，该配准措施适用于仅对一个图像可用分割的情况，例如，将与研究图像匹配以指导其分割的图集图像。通过最小化给定第二张图像中的类别标签的条件，第一张图像中的强度与另一张图像中的模糊类别标签相匹配。我们导出每个量度相对于各个体素位移的梯度的解析表达式，以得出驱动配准过程的力场，该力场由粘性流体模型进行正则化。在非刚性受试者间配准和基于图谱的MR脑图像分割的背景下评估基于类的度量的性能，并将其与仅使用强度信息的互信息最大化进行比较。我们的结果表明，在非刚性匹配后，将类别信息纳入配准措施可显着改善相应组织类别之间的重叠。此处提出的方法为在单个过程中集成分段和配准开辟了新的视角，从而将一个的输出用于指导另一个。

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