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Non-rigid medical image registration using image field in Demons algorithm

机译:在Demons算法中使用图像场进行非刚性医学图像配准

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In medical imaging application, non-rigid registration is an important step that requires both accuracy and efficiency to align the deformed query image with the reference image. For non-rigid registration, Demons is generally used as an effective algorithm. However, since the computational model of Demons uses only the gradient information, the directional information of the image has not been fully utilized. In this paper, we proposed a novel non-rigid registration method for processing medical images by introducing the image field to the Demons algorithm. As direction information is very important for spatial transformation in registration and the image fields contain the direction of the image, by combing image fields with the traditional model based registration algorithm, we introduce the orientation field directly to the model for estimating the distortion, and thus make a better use of the direction information of images and simplify the deformation model. Different performance measures are used to evaluate the qualitative measures of the proposed scheme. Experiments on brain images and fundus images show the improvement of the proposed algorithm compared to the other existing state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
机译:在医学成像应用中,非刚性配准是重要的步骤,需要准确和高效地将变形的查询图像与参考图像对齐。对于非刚性注册,通常将恶魔用作有效算法。但是,由于恶魔的计算模型仅使用梯度信息,因此图像的方向信息尚未得到充分利用。在本文中,我们通过将图像场引入恶魔算法中,提出了一种新颖的非刚性配准方法来处理医学图像。由于方向信息对于配准中的空间变换非常重要,并且图像场包含图像的方向,因此通过将图像场与传统的基于模型的配准算法进行组合,我们将定向场直接引入模型中以估计失真,因此更好地利用图像的方向信息,简化变形模型。使用不同的绩效指标来评估所提议方案的质量指标。在大脑图像和眼底图像上的实验表明,与其他现有的最新方法相比,该算法得到了改进。 (C)2019 Elsevier B.V.保留所有权利。

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