niftyreg.nonlinear(RNiftyReg)
niftyreg.nonlinear()所属R语言包：RNiftyReg
Two and three dimensional nonlinear image registration
二维和三维非线性图像配准
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The niftyreg.nonlinear function performs nonlinear registration for two and three dimensional images. 4D images may also be registered volumewise to a 3D image, or 3D images slicewise to a 2D image. The warping is based on free-form deformations, parameterised using an image of control points. A precalculated transformation can be applied to a new image using the applyControlPoints function.
niftyreg.nonlinear函数进行二维和三维图像的非线性登记。 “4D的图像，也可以注册到的3D图像或2D图像的3D图像slicewise volumewise。自由形式变形，参数化的使用的控制点的图像的基础上的翘曲。可以预先计算的转型applyControlPoints使用函数应用到一个新的形象。
用法----------Usage----------
niftyreg.nonlinear(source, target, targetMask = NULL, initAffine = NULL,
initControl = NULL, nLevels = 3, maxIterations = 300,
nBins = 64, bendingEnergyWeight = 0.01, jacobianWeight = 0,
finalSpacing = c(5,5,5), spacingUnit = c("vox","mm"),
finalInterpolation = 3, verbose = FALSE,
interpolationPrecision = NULL)
applyControlPoints(controlPointImage, source, target,
finalInterpolation = 3, interpolationPrecision = NULL)
参数----------Arguments----------
参数：source
The source image, an object of class "nifti" with 2, 3 or 4 dimensions. Package oro.nifti defines this class and provides functions for reading and writing NIfTI files.
源图像，对象类"nifti"2，3或4个维度。套件oro.nifti定义类，并提供NIfTI文件读取和写入功能。
参数：target
The target image, an object of class "nifti" with 2 or 3 dimensions.
目标图像，类"nifti"2维或3维的一个目的。
参数：targetMask
An optional mask image (again a "nifti" object), whose nonzero region will be taken as the region of interest for the registration. Must have the same voxel and image dimensions as the target image.
一个可选的掩模图像（再次"nifti"对象）的，其非零的区域将被视为用于登记感兴趣的区域。必须具有相同的体素和图像作为目标图像的尺寸。
参数：initAffine
An optional affine matrix, or list of matrices, to initialise the algorithm. If both this parameter and initControl are NULL, the identity matrix is used, with an appropriate offset to account for differences in the image origins.
一个可选的仿射矩阵，矩阵或列表，初始化算法。如果此参数和initControlNULL，单位矩阵使用，用适当的偏移量的差异在图像的起源。
参数：initControl
An optional image of class "nifti", or a list of images, representing fields of previously-calculated control points to use as initialisation for the algorithm. This parameter takes priority over initAffine if both are not NULL.
可选的图像类"nifti"，或列表的图像，代表领域的预先计算的控制点使用的算法初始化。此参数的优先级高于initAffine如果双方都没有NULL。
参数：nLevels
A single integer specifying the number of levels of the algorithm that should be applied. If zero, no optimisation will be performed, and the final control-point image will be the same as its initialisation value.
一个整数，指定的数量的算法，应适用。如果为零，没有优化将被执行，并且最终控制点图像作为其初始化值将是相同的。
参数：maxIterations
A single integer specifying the maximum number of iterations to be used within each level. Fewer iterations may be used if a convergence test deems the process to have completed.
一个单一的整数，用于指定要使用内每个级别的最大数目的迭代。更少的迭代收敛测试可以使用，如果认为过程已经完成。
参数：nBins
A single integer giving the number of bins to use for the joint histogram created by the algorithm.
一个单一的整数，箱数，使用的联合直方图的算法。
参数：bendingEnergyWeight
A numeric value giving the weight of the bending energy term in the cost function.
一个数字值，该值在成本函数中给出的重量的弯曲能量项。
参数：jacobianWeight
A numeric value giving the weight of the Jacobian determinant term in the cost function.
一个数字值的术语成本函数中的雅可比行列式的重量。
参数：finalSpacing
A numeric vector giving the spacing of control points in the final grid, along the X, Y and Z directions respectively. This is set from the initControl image if one is supplied.
给一个数字矢量控制点的间距，在最后一格，沿X，Y和Z方向分别。如果提供的话，这是从initControl图像。
参数：spacingUnit
A character string giving the units in which the finalSpacing is specified: either "vox" for voxels or "mm" for millimetres (which is assumed to be the spatial unit of the source and target images).
一个字符串的finalSpacing指定给单位：要么"vox"体素或"mm"毫米（被假定为空间单元的源和目标图像）。
参数：finalInterpolation
A single integer specifying the type of interpolation to be applied to the final resampled image. May be 0 (nearest neighbour), 1 (trilinear) or 3 (cubic spline). No other values are valid.
要施加到最终的重新采样的图像中的一个单一的整数，指定的内插类型。可以是0（近邻），1（三线性）或3（三次样条）。其它任何值都是有效的。
参数：verbose
A single logical value: if TRUE, the code will give some feedback on its progress; otherwise, nothing will be output while the algorithm runs.
一个单一的逻辑值：如果TRUE，代码将其取得的进展给予一定的反馈，否则什么也不会输出，同时该算法的运行。
参数：interpolationPrecision
The precision of the final, interpolated image: a single character string, or NULL. See Details.
最终的精度，内插的图像：一个字符串，或NULL。查看详细信息。
参数：controlPointImage
For applyControlPoints, the control point map to apply to the source image.
对于applyControlPoints，控制点的图应用的源图像。
Details
详细信息----------Details----------
This function performs the dual operations of finding a transformation to optimise image alignment, and resampling the source image into the space of the target image. Unlike niftyreg.linear, this transformation is nonlinear, and the degree of deformation may vary across the image.
执行该功能的双重操作找到的变换，以优化图像对准，和重采样源图像到目标图像的空间。不像niftyreg.linear，这种转变是非线性的，在图像上的变形程度可能会有所不同。
The nonlinear warping is based on free-form deformations. A lattice of equally-spaced control points is defined over the target image, each of which can be moved to locally modify the mapping to the source image. In order to assess the quality of the warping between the two images, an objective function based on the normalised mutual information is used, with penalty terms based on the bending energy or the squared log of the Jacobian determinant. The objective function value is optimised using a conjugate gradient scheme.
自由形式的变形的基础上的非线性翘曲。的格子等距控制点被定义在目标图像中，每一个都可以被移动到本地修改的源图像的映射。为了评估质量的两个图像之间的，一个目标函数的基础上的归一化互信息的翘曲被使用时，与惩罚项的基础上的雅可比行列式的弯曲能量或平方log。使用共轭梯度方案进行了优化的目标函数值。
The source image may have 2, 3 or 4 dimensions, and the target 2 or 3. The dimensionality of the target image determines whether 2D or 3D registration is applied, and source images with one more dimension than the target (i.e. 4D to 3D, or 3D to 2D) will be registered volumewise or slicewise, as appropriate. In the latter case the last dimension of the resulting image is taken from the source image, while all other dimensions come from the target. One image of control points is returned for each registration performed.
源图像可以具有2，3或4的尺寸，和目标2个或3个。目标图像的维数确定2D或3D的登记是否被施加，和源图像与目标（即4D到3D，或3D到2D）的尺寸比一个会被登记volumewise或slicewise，为适当的。在后者的情况下，所得到的图像被从源图像中的最后一个维度，而所有其他的尺寸来自目标。执行每注册一个影像控制点返回。
Greater precision may be appropriate for the final interpolated image than is used the source image. In particular, if the source image is integer-valued, then interpolation will generally produce nonintegral data values in the final image. The precision of the final image therefore defaults to being the same as the source image if nearest neighbour interpolation is requested (i.e. with finalInterpolation=0), and single-precision floating point otherwise. This default is chosen if the interpolationPrecision parameter is NULL: alternatively, one of "source" (for the same as the source image), "single" (for single-precision floating point) or "double" (for double-precision) may be specified explicitly.
更高的精度，可能是适当的最后的内插图像比使用的源图像。特别是，当源图像是整数值，那么插值一般会产生在最终图像中的非整数的数据值。最终的图像，因此默认的源图像是相同的，如果最近邻插值的要求（即finalInterpolation=0），单精度浮点否则的精度。如果interpolationPrecision参数是默认选择NULL：另外，"source"（相同的源图像），"single"（单精度浮点）或"double"（双精度）可能会显式地指定。
The applyControlPoints function is a convenience wrapper that calls niftyreg.nonlinear with nLevels=0 to apply the specified transformation without any further optimisation. Note that a target image must still be specified in this case, since the metadata associated with that image is needed by niftyreg.nonlinear.
applyControlPoints函数调用niftyreg.nonlinearnLevels=0申请指定的转换没有任何进一步的优化是一个方便的包装。注意仍然必须被指定的目标图像，在这种情况下，由于与该图像相关联的元数据所需要的niftyreg.nonlinear。
值----------Value----------
See niftyreg.
见niftyreg。
注意----------Note----------
Performing a linear registration first, and then initialising the nonlinear transformation with the result (via the initAffine parameter), is usually wise.
执行线性首次登记，然后初始化的非线性变换的结果（通过initAffine参数），通常是明智的。
If substantial parts of the target image are zero-valued, for example because the target image has been brain-extracted, it can be useful to pass it as a target mask as well as the target image, viz. niftyreg.nonlinear(source, target, target).
如果目标图像的实质性部分是零的值，例如，因为目标图像已被脑提取的，它可以是有用的，将它作为一个目标掩模，以及目标图像，即。 niftyreg.nonlinear(source, target, target)。
There is no reason that arrays that do not represent medical images cannot be registered using this function. A standard R array can be converted to a valid "nifti" object easily for these purposes using the as.nifti function in the oro.nifti package.
我们没有理由阵列，并不代表医学影像无法使用此功能注册。标准R数组可以被转换为有效的"nifti"对象很容易为这些目的使用as.nifti功能oro.nifti包。
（作者）----------Author(s)----------
Jon Clayden <jon.clayden+rniftyreg@gmail.com>
参考文献----------References----------
参见----------See Also----------
niftyreg, which can be used as an interface to this function, and niftyreg.linear for linear registration. See nifti (no relation!), in the oro.nifti package, for creating the image objects passed to this function. Useful related functions are as.nifti, readNIfTI and writeNIfTI.
niftyreg，它可以被用作此功能的接口，和niftyreg.linear的线性登记。见nifti（没有关系），在oro.nifti包，创造的形象传递给这个函数的对象。有用的相关功能as.nifti，readNIfTI和writeNIfTI。
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-27 19:49:16
