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Robust and Accurate Registration of 2-D Electrophoresis Gels Using Point-Matching

机译:使用点匹配技术对二维电泳凝胶进行稳健而准确的配准

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Point-matching is a widely applied image registration method and many algorithms have been developed. Registration of 2-D electrophoresis gels is an important problem in biological research that presents many of the technical difficulties that beset point-matching: large numbers of points with variable densities, large nonrigid transformations between point sets, paucity of structural information and large numbers of unmatchable points (outliers) in either set. In seeking the most suitable algorithm for gel registration we have evaluated a number of approaches for accuracy and robustness in the face of these difficulties. Using synthetic images we test combinations of three algorithm components: correspondence assignment, distance metrics and image transformation. We show that a version of the iterated closest point (ICP) algorithm using a non-Euclidean distance metric and a robust estimation of transform parameters provides best performance, equalling SoftAssign in the presence of moderate image distortion, and providing superior robustness against large distortions and high outlier proportions. From this evaluation we develop a gel registration algorithm based on robust ICP and a novel distance metric combining Euclidean, shape context and image-related features. We demonstrate the accuracy of gel matching using synthetic distortions of real gels and show that robust estimation of transform parameters using M-estimators can enforce inverse consistency, ensuring that matching results are independent of the order of the images
机译:点匹配是一种广泛应用的图像配准方法,并且已经开发了许多算法。二维电泳凝胶的配准是生物学研究中的一个重要问题,它带来了困扰点匹配的许多技术难题:密度可变的大量点,点集之间的大量非刚性转化,结构信息的匮乏以及大量的任何一组中都无法匹配的点(异常值)。在寻找最合适的凝胶配准算法时,面对这些困难,我们评估了许多方法的准确性和鲁棒性。使用合成图像,我们测试了三个算法组件的组合:对应分配,距离度量和图像变换。我们展示了使用非欧几里德距离度量和可靠的变换参数估计的迭代最近点(ICP)算法的一种版本可提供最佳性能,在存在中等图像失真的情况下与SoftAssign相等,并提供针对较大失真和高的异常值比例。通过此评估,我们开发了基于鲁棒ICP的凝胶配准算法以及结合欧几里得,形状上下文和图像相关特征的新颖距离度量。我们证明了使用真实凝胶的合成畸变进行凝胶匹配的准确性,并表明使用M估计器对转换参数进行可靠的估计可以强制实现逆一致性,从而确保匹配结果与图像顺序无关

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