**Volume Filter** includes several options for smoothing or transforming
volume data.
See also:
**Hide Dust**,
**vop**,
**mask**

There are several ways to start
**Volume Filter**, a tool in the **Volume Data** category
(including from the
**Volume Viewer**
**Tools** menu).

**Filter type**:

**Gaussian**- perform Gaussian filtering**Width**- width in physical units (such as Å) of the 3D isotropic Gaussian function

**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**vop**)**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****Laplacian**- perform Laplacian filtering**Fourier Transform**- apply Fourier transform**Scale**- change data values (not grid point locations) and/or types:**Shift**- add a constant to each value; can be negative**Scale**- multiply values by a factor**Value type**- cast values to int (8-, 16-, or 32-bit), uint (8-, 16-, or 32-bit), or float (32- or 64-bit)

**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.

Clicking **Options** reveals additional settings
(clicking the close button on the right
hides them again):

**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

**Gaussian 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 uses temporary memory
four times the size of the input data (if in 32-bit floating point values)
and produces a 32-bit floating point result. It may be helpful to limit
the input to just a subsample or subregion of the original data.

**Laplacian filtering**.
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.

**Fourier transform**.
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 / August 2009