Basics of the Coordinate Map

When you load an image it will have an associated Coordinate Map

Coordinate Map

The Coordinate Map contains information defining the input (domain) and output (range) Coordinate Systems of the image, and the mapping between the two Coordinate systems.

The input or domain in an image are voxel coordinates in the image array. The output or range are the millimetre coordinates in some space, that correspond to the input (voxel) coordinates.

>>> import nipy

Get a filename for an example file:

>>> from nipy.testing import anatfile

Get the coordinate map for the image:

>>> anat_img = nipy.load_image(anatfile)
>>> coordmap = anat_img.coordmap

For more on Coordinate Systems and their properties nipy.core.reference.coordinate_system

You can inspect a coordinate map:

>>> coordmap.function_domain.coord_names
>>> ('i', 'j', 'k')
>>> coordmap.function_range.coord_names
('aligned-x=L->R', 'aligned-y=P->A', 'aligned-z=I->S')

A Coordinate Map has a mapping from the input Coordinate System to the output Coordinate System

Here we can see we have a voxel to millimeter mapping from the voxel space (i,j,k) to the millimeter space (x,y,z)

We can also get the name of the respective Coordinate Systems that our Coordinate Map maps between.

A Coordinate Map is two Coordinate Systems with a mapping between them. Formally the mapping is a function that takes points from the input Coordinate System and returns points from the output Coordinate System. This is the same as saying that the mapping takes points in the mapping function domain and transforms them to points in the mapping function range.

Often this is simple as applying an Affine transform. In that case the Coordinate System may well have an affine property which returns the affine matrix corresponding to the transform.

>>> coordmap.affine
array([[ -2.,   0.,   0.,  32.],
       [  0.,   2.,   0., -40.],
       [  0.,   0.,   2., -16.],
       [  0.,   0.,   0.,   1.]])

If you call the Coordinate Map you will apply the mapping function between the two Coordinate Systems. In this case from (i,j,k) to (x,y,z):

>>> coordmap([1,2,3])
array([ 30., -36., -10.])

It can also be used to get the inverse mapping, or in this example from (x,y,z) back to (i,j,k):

>>> coordmap.inverse()([30.,-36.,-10.])
array([1., 2., 3.])

We can see how this works if we just apply the affine ourselves using dot product.


Notice the affine is using homogeneous coordinates so we need to add a 1 to our input. (And note how a direct call to the coordinate map does this work for you)

>>> coordmap.affine
array([[ -2.,   0.,   0.,  32.],
       [  0.,   2.,   0., -40.],
       [  0.,   0.,   2., -16.],
       [  0.,   0.,   0.,   1.]])
>>> import numpy as np
>>>, np.transpose([1,2,3,1]))
array([ 30., -36., -10.,   1.])


The answer is the same as above (except for the added 1)

Use of the Coordinate Map for spatial normalization

The Coordinate Map can be used to describe the transformations needed to perform spatial normalization. Suppose we have an anatomical Image from one subject subject_img and we want to create an Image in a standard space like Tailarach space. An affine registration algorithm will produce a 4-by-4 matrix representing the affine transformation, T, that takes a point in the subject’s coordinates subject_world to a point in Tailarach space tailarach_world. The subject’s Image has its own Coordinate Map, subject_cmap and there is a Coordinate Map for Tailarach space which we will call tailarach_cmap.

Having found the transformation matrix T, the next step in spatial normalization is usually to resample the array of subject_img so that it has the same shape as some atlas atlas_img. Note that because it is an atlas Image, tailarach_camp=atlas_img.coordmap.

A resampling algorithm uses an interpolator which needs to know which voxel of subject_img corresponds to which voxel of atlas_img. This is therefore a function from atlas_voxel to subject_voxel.

This function, paired with the information that it is a map from atlas-voxel to subject-voxel is another example of a Coordinate Map. The code to do this might look something like the following:

>>> from nipy.testing import anatfile, funcfile
>>> from nipy.algorithms.registration import HistogramRegistration
>>> from nipy.algorithms.kernel_smooth import LinearFilter

We’ll make a smoothed version of the anatomical example image, and pretend it’s the template

>>> smoother = LinearFilter(anat_img.coordmap, anat_img.shape)
>>> atlas_im = smoother.smooth(anat_img)
>>> subject_im = anat_img

We do an affine registration between the two.

>>> reggie = HistogramRegistration(subject_im, atlas_im)
>>> aff = reggie.optimize('affine').as_affine() 
Initial guess...

Now we make a coordmap with this transformation

>>> from nipy.core.api import AffineTransform
>>> subject_cmap = subject_im.coordmap
>>> talairach_cmap = atlas_im.coordmap
>>> subject_world_to_talairach_world = AffineTransform(
...                                       subject_cmap.function_range,
...                                       talairach_cmap.function_range,
...                                       aff)

We resample the ‘subject’ image to the ‘atlas image

>>> from nipy.algorithms.resample import resample
>>> normalized_subject_im = resample(subject_im, talairach_cmap,
...                                  subject_world_to_talairach_world,
...                                  atlas_im.shape)
>>> normalized_subject_im.shape == atlas_im.shape
>>> normalized_subject_im.coordmap == atlas_im.coordmap
>>> np.all(normalized_subject_im.affine == atlas_im.affine)

Mathematical definition

For a more formal mathematical description of the coordinate map, see Mathematical formulation of the Coordinate Map.