This action crops a part of an image volume. Use the GUI to select the volume to crop. You can choose either the min and the max coordinates of each axis (i,j,k), i.e., 6 points or use only two extreme 3D points (the origin of parallelepiped and its opposite diagonal point).

• Zero order: no derivation
• First order derivative
• Second order derivative

• Derivative along X axis
• Derivative along Y axis
• Derivative along Z axis

• erosion
• dilation
• opening
• closure

Blurring is the traditional apporach for removing noise from images. It is usually implemented in the form of a convolution with a kernel. One of the most commonly used kernels is the Gaussian.

The classical methode of smoothing an image by convolution with a Gaussian kernel has the drawback that it is slow when the standard deviation sigma of the Gaussian is large.

The Recursive IIR (Infinite Impulse Rewponse) implements an approximation of convolution with the Gaussian. In practice, this filter requires a constant number of operations for approximating the convolution, regardless of the sigma value.

Anisotropic diffusion methods are formulated to reduce noise (or unwanted detail) in images while preserving specific image features. For many applications, there is an assumption that light-dark transitions (edges) are interesting. Standard isotropic diffusion methods move and blur light-dark boundaries. Anisotropic diffusion methods are formulated to specifically preserve edges.

The numberOfIterations parameter specifies the number of iterations (time-step updates) that the solver will perform to produce a solution image. The appropriate number of iterations is dependent on the application and the image being processed. As a general rule, the more iterations performed, the more diffused the image will become.

The conductance parameter controls the sensitivity of the conductance term in the basic anisotropic diffusion equation. It affect the conductance term in different ways depending on the particular variation on the basic equation. As a general rule, the lower the value, the more strongly the diffusion equation preserves image features (such as high gradients or curvature). A high value for conductance will cause the filter to diffuse image features more readily. Typical values range from 0.5 to 2.0 for data like the Visible Human color data, but the correct value for your application is wholly dependent on the results you want from a specific data set and the number or iterations you perform.

The Gradient anisotropic diffusion implements an N-dimensional version of the classic Perona-Malik anisotropic diffusion equation for scal-valued images.

The Curvature anisotropic diffusion performs anisotropic diffusion on an image using a modified curvature diffusion equation (MCDE). MCDE does not exhibit the edge enhancing properties of classic anisotropic diffusion, which can under certain conditions undergo a negative diffusion, which enhances the contrast of edges. Equations of the form MCDE always undergo positive diffusion, with the conductance term only varying the stregth of that diffusion.

This filter creates a binary thresholded image that separates an image into foreground and background components.
The filter calculates the optimum threshold separating those two classes so that their combined spread (intra-class variance) is minimal (see http://en.wikipedia.org/wiki/Otsu%27s_method). Then the filter applies that threshold to the input image using an Itk binary fileter. The numberOfHistogram bins can be set for the Otsu Calculator. The insideValue and outsideValue can be set for the Itk binary filter. The filter produces a labeled volume.

The original reference is:
. Otsu, ''A threshold selection method from gray level histograms,'' IEEE Trans.Syst.ManCybern.SMC-9,62-66 1979.

• If the initial value is between low and high thresholds (included), the new voxel value will be set to 255 (usually displayed as white)
• Otherwise the new voxel value is set to 0 (usually displayed as black)

If you want to select all the voxels that have a specific value x, set both thresholds to the value x.

The current points selected by picking (Picked Selection) will be considered as fixed nodes.

• topology modification is allowed (i.e., the parameter Preserve topology is false);
• mesh splitting is enabled (i.e., the parameter Splitting is true);
• the algorithm is allowed to modify the boundaries of the mesh (i.e., the parameter Boundary vertex deletion? is true);
• the maximum allowable error (i.e., the parameter Maximum error) is set to 1.0e+38f (default value).

• The first one is the source mesh (the one to be registered)
• The second one is the target mesh

• If points are exactly at the same coordinates, they will be merged.
• If cells (tetras, triangles) are overlapped, just one is kept (Points must be exactly at the same coordinates).
• If a triangle is enclosed in a volume during the merging action, it will be removed.

• A size of 1 (X, Y or Z) corresponds to one pixel in the considered slice.
• A size of 2 corresponds to 3 pixels in the slice, in the aim to always keep the picked pixel as the center.
  (Size 3-> 5pix; 4->7pix...)

Registration using Elastix

Note that elastix should be installed on your machine.

This action can only modifies the following three main parameters:

• Initialization: whether or not the initial translation between images should be estimated as the distance between their centers.
• Transformation model: determines what type of deformations between the fixed and moving image is handled. There are 6 models available in elastix. Only three different types are available in this action: translation only, affine transformation (includes translation, rotation, scaling and shearing, and the nonrigid (i.e., elastic) B-spline transformation (uses cubic multidimensional B-spline polynomial control points). See section "2.6 Transform" of the elastix manual.
• Similarity metrics: the similarity measure uses to compare the voxel values and find the best match during the registration. There are 5 different similarity measures available in elastix, the main one being: Mean Squared Difference (MSD) and Mutual Information (MI). See section "2.3 Metrics" of the elastix manual.

• 1. Resamples the scalar type of the voxels unless new scalard type is SAME_AS_INPUT
• 2. Resamples the size of the voxels

• If the initial value is between low and high thresholds (included), the new voxel value will be set to 'In Value' (e.g., 255 that is usually displayed as white)
• Otherwise the new voxel value is set to 'Out Value' (e.g., 0 that is usually displayed as black)

If you want to select all the voxels that have a specific value x, set both thresholds to the value x. You can replace the in values, the out values or both with the given values (note: replacing both, the default, will result in a binary image).

• Left button: grab on the six face handles → slide the faces
• Left button: grab the center handle → move the entire box
• Shift+Left button: inside the box widget → Translation
• Right button: inside the box widget → up/down scaling
• Left mouse: pick a face (not a face handle) → rotate (if Rotation Enabled is ticked)

• Click Apply and then left click in the chosen viewer to add the first contour node
• Left Click: Add/select a point
• Right Click: Add final point
• Middle Click: Translate
• Delete Contour Widget: Delete the current contour (keeps the generated mesh component)
• Close Contour Widget: Either join start/end points interactively or click the push button