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Improving an affine and non—linear image registration and/or segmentation task by incorporating characteristics of the displacement field

机译:通过结合位移场的特征来改进仿射和非线性图像配准和/或分割任务

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Image registration is an important and active area of medical image processing. Given two images, the idea is to compute a reasonable displacement field which deforms one image such that it becomes similar to the other image. The design of an automatic registration scheme is a tricky task and often the computed displacement field has to be discarded, when the outcome is not satisfactory. On the other hand, however, any displacement field does contain useful information on the underlying images. It is the idea of this note, to utilize this information and to benefit from an even unsuccessful attempt for the subsequent treatment of the images. Here, we make use of typical vector analysis operators like the divergence and curl operator to identify meaningful portions of the displacement field to be used in a follow-up run. The idea is illustrated with the help of academic as well as a real life medical example. It is demonstrated on how the novel methodology may be used to substantially improve a registration result and to solve a difficult segmentation problem.
机译:图像配准是医学图像处理的重要且活跃的区域。给定两个图像,想法是计算一个合理的位移字段,其变形一个图像,使得它变得与另一个图像类似。自动注册方案的设计是一个棘手的任务,并且当结果不令人满意时,必须丢弃计算的位移场。然而,另一方面,任何位移字段都确实包含关于底层图像的有用信息。这是本说明的想法,利用这些信息,并从甚至不成功的尝试中受益于后续治疗图像。在这里,我们利用典型的载体分析运营商,如发散和卷曲操作员,以识别以在后续运行中使用的位移场的有意义部分。这些想法是在学术的帮助下说明的,也是真实的医学例子。据证明了如何使用新颖的方法来基本上改善登记结果并解决困难的分割问题。

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