Least median of squares filtering of locally optimal point matches for compressible flow image registration.
Least median of squares filtering of locally optimal point matches for compressible flow image registration.
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DOI：
10.1088/0031-9155/57/15/4827
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发表时间：
2012-08-07
影响因子：
3.5
通讯作者：
Guerrero T
中科院分区：
文献类型：
--
作者：
Castillo E;Castillo R;White B;Rojo J;Guerrero T
基于可压缩流的图像配准是在成像材料的质量从一幅图像到下一幅图像守恒的假设下进行的。根据质量守恒假设的建模方式，现有可压缩流方法的性能受到图像质量、噪声、大幅度体素位移和计算要求等因素的限制。这里介绍的最小二乘中值滤波可压缩流 (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，适合简单的网格搜索（块匹配）优化策略。空间不准确的网格搜索点匹配（对应于非线性可压缩流模型的错误局部最小化器）通过基于最小二乘中值拟合和前向搜索异常值检测方法的新颖过滤方法来消除。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标志的大样本（可在 www.dir-lab.com 上获取）来测量的。 LFC 方法在十个案例中的八个案例中产生了观察者内误差内的平均误差，表明该方法能够实现胸部 CT 配准的高空间精度。
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.
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