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首页> 外文期刊>Physics in medicine and biology. >Least median of squares filtering of locally optimal point matches for compressible flow image registration
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Least median of squares filtering of locally optimal point matches for compressible flow image registration

机译：用于可压缩流图像配准的局部最优点匹配的最小二乘平方滤波

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摘要

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration.
机译：基于可压缩流的图像配准是在假设从一个图像到另一个图像都保留了成像材料的质量的前提下进行的。根据质量守恒假设的建模方式，现有的可压缩流方法的性能受到图像质量，噪声，大尺寸体素位移和计算要求等因素的限制。此处介绍的最小二乘滤波可压缩流（LFC）方法基于局部非线性最小二乘可压缩流模型，该模型描述单个体素的位移，从而使自身适合于简单的网格搜索（块匹配）优化策略。通过基于最小二乘平方拟合的新颖滤波方法和正向搜索异常值检测方法，可以消除与非线性可压缩流模型的错误局部最小化相对应的空间不准确的网格搜索点匹配。该方法的空间精度是使用十个胸部CT图像集和专家确定的地标的大量样本（可从www.dir-lab.com获得）测量的。 LFC方法在十种情况中的八种情况下会在观察者内部误差范围内产生平均误差，这表明该方法能够实现胸部CT对位的高空间精度。

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