Image Filters Panel
Filter image using different image filters.
Allows selection of a filter from a list of 2D and 3D image filters. Depending on the filter type some additional parameters should be specified in the HSize, Sigma, lambda, Type, Angle, Iter edit boxes.
List of available filters:
- Gaussian, (2D) Matlab a rotationally symmetric Gaussian lowpass filter, see more in the Matlab documentation for fspecial and imfilter.
- Gaussian 3D, (3D) is based on Dirk-Jan Kroon implementation and uses the fact that a Gaussian kernel can be implemented as several 1D kernels.
- Perona Malik anisotropic diffusion, (2D) - a filter written by Peter Kovesi to perform anisotropic diffusion of an image following Perona and Malik's algorithm. This process smoothes the regions while preserving, and enhancing the contrast at sharp intensity gradients.
- Average, (2D) Matlab Averaging filter, see more in the Matlab documentation for fspecial and imfilter.
- Disk, (2D) Matlab circular averaging filter (pillbox), see more in the Matlab documentation for fspecial and imfilter.
- Gradient, (2D) generates gradient image for the shown orientation.
- Gradient, (3D) generates gradient image for the whole volume.
- Frangi 2D/3D, Hessian based Frangi Vesselness filter. This function uses the eigenvectors of the Hessian to compute the likeliness of an image region to contain vessels or other image ridges , according to the method described by Frangi 1998, 2001. Implementation is based on Hessian based Frangi Vesselness filter, written by Marc Schrijver and Dirk-Jan Kroon.
- Motion, (2D) Matlab filter to approximate the linear motion of a camera, see more in the Matlab documentation for fspecial and imfilter.
- Unsharp, (2D) Matlab sharpens image using unsharp masking (imsharpen function, R2013a and above) or unsharpens contrast enhancement filter (fspecial and imfilter, R2012b and older).
- Median 2D, (2D) Matlab 2D median filter. Median filtering is a nonlinear operation often used in imageprocessing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. See more in the Matlab documentation for medfilt2.
- Wiener 2D, (2D) Matlab 2D 2-D adaptive noise-removal filtering (wiener2 function). wiener2 lowpass-filters a grayscale image that has been degraded by constant power additive noise. wiener2 uses a pixel wise adaptive Wiener method based on statistics estimated from a local neighbourhood of each pixel.
- Edge Enhancing Coherence Filter, (2D and 3D) based on the Image Edge Enhancing Coherence Filter Toolbox written by Dirk-Jan Kroon and Pascal Getreuer. Check here for for details.
- Diplib filters - [optional, requires additional installation ] a number of algorithms that are provided with DIPlib library. Note, to work properly the toolbox should be installed. See details in the System Requirements.
Note! If HSize is specified with a single number then the size of the 3D Kernel is calculated based on pixel size of the dataset Menu->Dataset->Parameters. If HSize is specified with 2 numbers (i.e. 3;3) then the Kernel size is [3 x 3 x 3].
The Mode combobox allows to select part of the open dataset to apply the filters.
- 2D, shown slice, apply filter only for the currently shown slice
- 3D, current stack, apply filter only for the currently shown stack
- 4D, complete volume, apply filter for complete dataset
The Options combobox allows to choose what to do with the dataset after filtration
- Apply filter, just filter the image and show result on the screen
- Apply and add to the image, filter the image and add the result to the original image
- Apply and subtract from the image, filter the image and subtract the result from the original image
Here is a set of edit boxes (Type, HSize, lambda, Sigma, beta2, beta3) that define additional parameters for the filters. Depending on the selected filter one or more of these edit boxes may be disabled.
Press this button to start the filtering.