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Rewrite `center_of_mass` using `labeled_comprehension` (#78)

* Add a custom kernel for `center_of_mass`

Create a custom kernel of `center_of_mass` to use with
`labeled_comprehension`. In the kernel take the ROI's intensity and 1-D
positions along with a few arguments to be bound before calling
`labeled_comprehension` (like the shape of the original image and the
results expected type). Use these to unravel the 1-D positions back into
N-D positions and computed the center of mass for the given ROI to pass
back in an expected form. As `labeled_comprehension` can only handle
scalars being returned for each ROI, use a structured type to pass back
each coordinate of the center of mass in a different field. The
structured type uses a simple naming scheme based on a string holding
the index corresponding to that dimension.

* Use `labeled_comprehension` in `center_of_mass`

Rewrite `center_of_mass` to use `labeled_comprehension` along with a
custom kernel. This simplifies the Dask graph significantly, improves
the performance noticeably, and cleans up the code a bit as the whole
center of mass computation is now handled by the custom kernel function.
Only a little bit of bookkeeping is needed to bind a few extra arguments
(since `labeled_comprehension` does not handle keyword arguments) to the
custom kernel and to convert the custom structured array type (needed
since `labeled_comprehension` cannot handle results with a shape) into a
normal array before returning it user.

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