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Nonrigid medical image registration technique as a composition of local warpings

机译:非刚性医学图像配准技术作为局部翘曲的组成

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摘要

We introduce a new technique for nonrigid image registration based on the composition of local deformations. The warping model is analyzed in order to guarantee continuity, differentiability and a one-to-one transformation by constraining the parameters of the nonlinear spatial transformation. A genetic algorithm solves the model by global optimization, handling constraints, and maximizing the normalized mutual information. The composition of local transformations goes throughout several levels of resolution, from coarse to fine. The performance of our technique was tested in synthetic and real medical images. The proposed method was always able to improve the similarity criterion between image pairs, demonstrating the robustness of the method for several modalities of images. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:我们介绍了一种基于局部变形组成的非刚性图像配准的新技术。通过限制非线性空间变换的参数,对翘曲模型进行分析,以确保连续性,可微性和一对一变换。遗传算法通过全局优化,处理约束和最大化标准化互信息来求解模型。从粗略到精细,局部变换的组成贯穿于多个分辨率级别。我们的技术性能已在合成和真实医学图像中进行了测试。所提出的方法总是能够改善图像对之间的相似性标准,证明了该方法对多种图像模态的鲁棒性。 (C)2004模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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