用于图像配准的结构图像表示

K. Aghajani, Mohsen Shirpour, M. T. Manzuri
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引用次数: 0

摘要

图像配准是医学图像处理中的一项重要任务。假设图像之间的强度关系在空间上是平稳的,那么传统的基于区域的算法(如CC (Correlation Coefficients)和MI (Mutual Information))在空间变化的强度失真下显示出较弱的结果。在本研究中,引入了图像的结构表示。它允许我们在多模态图像中使用更简单的相似性度量,这些度量对上述失真场也具有鲁棒性。研究了该方法在存在空间变化畸变场的非刚性图像配准中的有效性。在合成数据集和真实数据集上的实验结果证明了该方法对图像配准任务的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural image representation for image registration
Image registration is an important task in medical image processing. Assuming spatially stationary intensity relation among images, conventional area based algorithms such as CC (Correlation Coefficients) and MI (Mutual Information), show weaker results alongside spatially varying intensity distortion. In this research, a structural representation of images is introduced. It allows us to use simpler similarity metrics in multimodal images which are also robust against the mentioned distortion field. The efficiency of this presentation in non-rigid image registration in the presence of spatial varying distortion field is examined. Experimental results on synthetic and real-world data sets demonstrate the effectiveness of the proposed method for image registration tasks.
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