Convolution

MLModule

genre	Kernel
author	MeVis Medical Solutions AG
package	MeVisLab/Standard
dll	MLKernel
definition	MLKernel.def
see also	ExtendedConvolution, Rank, KernelEditor, Morphology, BitMorphology, KernelExample, Sigma, LoG
keywords	enhance, kernel, input, filter, laplace, gauss, sobel, average, smooth, edge, interval, sphere, spherical, structure, element

Purpose

The module Convolution implements generic kernel-based filtering (linear and shift-invariant).

Details

The implemented kernel functions are defined by

where g denotes the output, f the input, t the kernel and k is a 6D multi-index.

Please note that in this module not a point spread function (impulse response) is supplied by the user but a kernel function. The point spread function h is related to the kernel via mirroring (origin is the kernel center):

One can use some simple pre-defined kernels like Average, Gauss, Laplace and Sobel, or supply a custom defined kernel via the kernel-editor module.

Optionally, a threshold interval can be used to select a subset of voxels of the input image to which the filtering is applied. By using this threshold interval the filtering can be applied selectively to regions in the image.

Windows

Default Panel

Input Fields

input0

name: input0, type: Image

Output Fields

output0

name: output0, type: Image

Parameter Fields

Field Index

Border Handling: Enum	Use (useIntervalOfFilteredVoxels): Bool
External Kernel: String	Use (predefKernel): Enum
Fill Value: Double
Kernel Output: String
kernelInfo: String
Max: Double
Min: Double
referenceExtentMode: Enum

Visible Fields

Border Handling

name: borderHandling, type: Enum, default: PadSrcClamp, deprecated name: edgeMode

Defines the border handling mode.

See Border Handling in Kernel Operations for details.

Values:

Title	Name
No Pad	NoPad
Pad Src Fill	PadSrcFill
Pad Dst Fill	PadDstFill
Pad Dst Fill With Orig	PadDstFillWithOrig
Pad Src Undefined	PadSrcUndefined
Pad Dst Undefined	PadDstUndefined
Pad Src Clamp	PadSrcClamp

Fill Value

name: fillValue, type: Double, default: 0

Sets the fill value for certain Border Handling modes.

Min

name: intervalMinOfFilteredVoxels, type: Double, default: 0, deprecated name: ImageIntervalMin

Sets the minimum value threshold for outputting a subset of voxel.

Max

name: intervalMaxOfFilteredVoxels, type: Double, default: 1024, deprecated name: ImageIntervalMax

Sets the maximum value threshold for outputting a subset of voxel.

Use (useIntervalOfFilteredVoxels)

name: useIntervalOfFilteredVoxels, type: Bool, default: FALSE, deprecated name: UseImageInterval

If checked, the module outputs only on a subset of voxels defined by a value range.

External Kernel

name: externalKernel, type: String, deprecated name: kernelInput

Sets an external kernel as a string.

Use the module KernelEditor for editing the kernel or set the kernel by scripting.

Kernel Output

name: kernelOutput, type: String, persistent: no

Shows the used kernel as a string for connecting to other modules that use an external kernel.

Use (predefKernel)

name: predefKernel, type: Enum, default: 3x3 Average Kernel

Defines the applied filter.

Values:

Title	Name	Description
3x3 Average Kernel	3x3 Average Kernel	All Elements of this kernel have normalized and equal coefficents. 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9
5x5 Average Kernel	5x5 Average Kernel	All Elements of this kernel have normalized and equal coefficents. 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25
7x7 Average Kernel	7x7 Average Kernel	All Elements of this kernel have normalized and equal coefficents.
10x10 Average Kernel	10x10 Average Kernel	All Elements of this kernel have normalized and equal coefficents.
25x25 Average Kernel	25x25 Average Kernel	All Elements of this kernel have normalized and equal coefficents.
3x3 Gauss Kernel	3x3 Gauss Kernel	Applies a 3x3 Gaussian kernel. Smoothes image and emphasizes the inner voxels. 1/16 2/16 1/16 2/16 4/16 2/16 1/16 2/16 1/16
5x5 Gauss Kernel	5x5 Gauss Kernel	Applies a 5x5 Gaussian kernel.
Laplace Kernel	Laplace Kernel	Applies a 3x3 Laplacian kernel. 0 1 0 1 -4 1 0 1 0
Laplace2 Kernel	Laplace2 Kernel	Applies a 3x3 Laplacian kernel for edge detection. This one is taking the second derivation into account. 1 2 1 2 -12 2 1 2 1
Sobel1 Kernel |	Sobel1 Kernel |	Applies a 3x3 Sobel1 kernel. Used for edge detection in vertical direction and smoothing in horizontal direction. 1 0 -1 2 0 -2 1 0 -1
Sobel2 Kernel -	Sobel2 Kernel -	Applies a 3x3 Sobel2 kernel. Used for edge detection in horizontal direction and smoothing in vertical direction. 1 2 1 0 0 0 -1 -2 -1
Sobel3 Kernel	Sobel3 Kernel	Applies a 3x3 Sobel3 kernel. Used for edge detection in diagonal direction and smoothing in contrary direction. 0 -1 -2 1 0 -1 2 1 0
Sobel4 Kernel /	Sobel4 Kernel /	Applies a 3x3 Sobel4 kernel. Used for edge detection in diagonal direction and smoothing in contrary direction. -2 -1 0 -1 0 1 0 1 2
Id Kernel	Id Kernel	Just copies the input image to the output.
From Kernel Input	From Kernel Input	Uses the kernel specified in the kernel input field. Usually this will be a kernel passed via a field connection from the text field of a kernel editor. Note that a kernel defined once can be saved/loaded with the operator state normally even if it is not visible as kernel matrix. Please note that the kernel editor specifies a kernel function, not a point spread function.

Hidden Fields

referenceExtentMode

name: referenceExtentMode, type: Enum, default: Overlap

Values:

Title	Name
Overlap	Overlap
Input0 Ext Without Fill	Input0_ExtWithoutFill
Input0 Ext With Fill	Input0_ExtWithFill
Max Extents Without Fill	MaxExtentsWithoutFill
Max Extents With Fill	MaxExtentsWithFill

kernelInfo

name: kernelInfo, type: String, persistent: no