Volume Filter includes several options for smoothing or transforming
There are several ways to start
Volume Filter, a tool in the Volume Data category
(including from the
processes the current set of data
in Volume Viewer.
If the current set is the result of a previous application of the same type
of filtering, it will simply be updated to reflect the new settings (the
original data are re-filtered with the new settings).
Otherwise, a new volume data set will be created and made the current set.
The new data set can be
saved to a file with
- Gaussian - perform Gaussian filtering
- Width - one standard deviation in physical units (such as Å)
of the 3D isotropic Gaussian function
- Value type - whether to produce a map with the same
as the input, 32-bit float, or 64-bit float
- Median 3x3x3 - smooth the data by setting each value to the
median of the 27 values in the enclosing 3x3x3 box of grid cells
(median filtering with different box sizes can be performed with
- Iterations - how many cycles of value reassignment to perform
- Bin - reduce data size by averaging the values in
NxNxN cells of the original data, where N is the Bin size.
Different bin sizes along the different axes can be specified by
entering three numbers separated by spaces.
- Laplacian - perform Laplacian filtering
- Fourier Transform - apply Fourier transform
- Scale - change data values (not grid point locations) and/or types:
Pressing return (Enter) in the Shift or Scale value field is
equivalent to clicking Filter.
Shifting and scaling are applied in that order.
Shifting and scaling use 32-bit float to avoid truncation problems.
- Shift - add a constant to each value; can be negative
- Scale - multiply values by a factor
- Value type
- cast values to a different
int (8-, 16-, or 32-bit), uint (8-, 16-, or 32-bit), or float (32- or 64-bit)
- Flatten - scale data values by factor (1 + a*i + b*j + c*k)
where i,j,k are the grid indices and a,b,c are calculated to
zero out the first moments of the resulting map
(make its mass balance at the center of the grid)
Clicking Options reveals additional settings
(clicking the close button on the right
hides them again):
Close dismisses the Volume Filter dialog; Help
opens this manual page in a browser window.
- Displayed subregion only
- whether to limit the procedure to the
current display region
(which could be a subregion of
the current set)
- Displayed subsampling only
- whether to use only the displayed subsample
(when step > 1)
instead of the full resolution of the current set
- Adjust threshold for constant volume - adjust the contour level on
the result to enclose the same volume as the contour surface on the current set
- Immediate update - update the result as soon as
a slider parameter (Gaussian width or median iterations) is changed;
only applies when the current set
is the result of a previous application of the same type of filtering
Convoluting the data with a Gaussian function
improves the ratio of signal to noise but reduces resolution.
It is fastest for data sizes that are powers of 2, and can be very slow
when insufficient memory is available.
It produces a a map with 32-bit floating point values and uses
negligible additional memory. It may be helpful to limit
the input to just a subsample or subregion of the original data.
Although it uses a fast Fourier transform calculation method,
it does not use map periodicity.
Values outside the map boundaries are treated as zero.
The Laplacian operation is a sum of second derivatives.
Laplacian filtering is useful for edge detection but
amplifies noise, so it may be necessary to perform smoothing such as
Gaussian filtering beforehand.
Finite differences v(i-1)-2*v(i)+v(i+1) along each axis are used,
and voxels at the edge of the box are set to zero.
Only the magnitudes of the complex Fourier components are included in the
new data set; the phases are discarded and the constant component is set to
zero. The box containing the Fourier transform
(with axes in units of reciprocal space) is centered on the original data
and scaled to have the same total volume.
Some properties of the original data are evident from the Fourier transform.
High-frequency components are near the edges of the box,
low-freqency components near the center.
Volume data is typically oversampled (voxel size two to three times smaller
than the actual data resolution) and this causes the Fourier transform
to have nonzero values only in the middle half or third of its bounding box.
The missing wedge in electron microscope tomograms can also be seen.
Spikes radiating along the principal axes in the Fourier transform are
caused by nonperiodicity of the original data.
UCSF Computer Graphics Laboratory / September 2010