niftyreg.linear(RNiftyReg)
niftyreg.linear()所属R语言包：RNiftyReg
Two and three dimensional linear image registration
二维和三维线性图像配准
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The niftyreg.linear function performs linear 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. Rigid-body (6 degrees of freedom) and affine (12 degrees of freedom) registration can currently be performed. A precalculated transformation can be applied to a new image using the applyAffine function.
niftyreg.linear函数进行二维和三维图像的线性登记。 “4D的图像，也可以注册到的3D图像或2D图像的3D图像slicewise volumewise。目前可以进行刚体（6自由度）和仿射（12个自由度）注册。可以预先计算的转型applyAffine使用函数应用到一个新的形象。
用法----------Usage----------
niftyreg.linear(source, target, targetMask = NULL, initAffine = NULL,
scope = c("affine","rigid"), nLevels = 3, maxIterations = 5,
useBlockPercentage = 50, finalInterpolation = 3,
verbose = FALSE, interpolationPrecision = NULL)
applyAffine(affine, source, target, affineType = NULL,
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 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NULL，使用单位矩阵，用适当的偏移以用于图像的起源的差异。
参数：scope
A string describing the scope, or number of degrees of freedom (DOF), of the registration. Only "affine" (12 DOF) and "rigid" (6 DOF) are currently supported.
一个字符串，它描述的范围或数量的自由度（DOF），注册登记手续。只有"affine"（12 DOF）和"rigid"（6 DOF），目前支持。
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 affine matrix 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，并且最终的仿射矩阵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：useBlockPercentage
A single integer giving the percentage of blocks to use for calculating correspondence at each step of the algorithm. The blocks with the highest intensity variance will be chosen.
一个单一的整数，给出的块的百分比使用的算法，用于计算对应于每个步骤。具有最高强度的方差的块将被选择。
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：affine
For applyAffine, the affine transformation(s) to apply to the source image.
对于applyAffine，仿射变换（S）适用于源图像。
参数：affineType
For applyAffine, the storage convention type of the affine matrix, if it is not stored in the affineType attribute of the matrix.
对于applyAffine，仿射矩阵的公约的存储类型的，如果它不存储在affineType属性的矩阵。
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.
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。
The algorithm is based on a block-matching approach and Least Trimmed Squares (LTS) fitting. Firstly, the block matching provides a set of corresponding points between a target and a source image. Secondly, using this set of corresponding points, the best rigid or affine transformation is evaluated. This two-step loop is repeated until convergence to the best transformation.
该算法是基于块匹配方法和最不修剪法（LTS）配件。首先，块匹配，提供了一组的一个目标和源图像之间的对应点。其次，使用该组对应点，最好的刚性或仿射变换进行了评价。这两个步骤的循环重复进行，直到收敛到最佳变换。
In the NiftyReg implementation, normalised cross-correlation between the target and source blocks is used to evaluate correspondence. The block width is constant and has been set to 4 voxels. A coarse-to-fine approach is used, where the registration is first performed on down-sampled images (using a Gaussian filter to resample images), and finally performed on full resolution images.
在NiftyReg实施，归一化交叉相关之间的目标块和源块是用来评估对应。该块的宽度是恒定的，并已被设置为4的体素。甲粗到细的方法使用，在注册时，首先执行下采样的图像（使用高斯滤波器进行重采样图像），并最后进行全分辨率的图像。
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 affine matrix 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。返回一个仿射矩阵的每个注册进行。
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 applyAffine function is a convenience wrapper that calls niftyreg.linear 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.linear.
applyAffine函数调用niftyreg.linearnLevels=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.linear。
值----------Value----------
See niftyreg.
见niftyreg。
注意----------Note----------
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.linear(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.linear(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.nonlinear for nonlinear 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.nonlinear为非线性登记。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 19:49:05
