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Multi-modal image registration based on diffeomorphic demons algorithm

机译:基于微变形魔鬼算法的多峰图像配准

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Multi-modality image registration plays an important role in the domain of medical image processing. Diffeomorphic demons method has been proven to be a robust and efficient way for single mode image registration. However, it cannot deal with multi-modality image. In this paper we introduce mutual information into diffeomorphic demons method. On the basis of original force for driving image deformation, the proposed method adds mutual information gradient on the current transformation and adds mutual information into the energy function. We compare the performance of image registration results among our proposed method, diffeomorphic demons method and B-spline based free form deformation method in combination with mutual information. Experiment shows that our proposed method gives the better results like the smallest registration errors in case of local distortions. In conclusion, our proposed method has good performance in dealing with local deformation multi-model image registration.
机译:多模态图像配准在医学图像处理领域中起着重要作用。拟态恶魔方法已被证明是一种用于单模图像配准的鲁棒且有效的方法。但是,它不能处理多模态图像。在本文中,我们将互信息引入到微分恶魔方法中。在驱动图像变形的原力的基础上,该方法在电流变换上增加了互信息梯度,并将互信息加入了能量函数。我们在结合互信息的基础上,比较了我们提出的方法,微变形魔鬼方法和基于B样条的自由形式变形方法的图像配准结果的性能。实验表明,在局部失真的情况下,我们提出的方法给出了更好的结果,例如最小的配准误差。总之,我们提出的方法在处理局部变形多模型图像配准方面具有良好的性能。

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