11.68. ITK Signed Maurer Distance Map Image Filter
Computes an exact Euclidean signed distance map of a binary image in linear time.
Group (Subgroup)
ITKDistanceMap (DistanceMap)
Description
A distance map replaces every pixel with its distance to the nearest object boundary. This filter computes the exact Euclidean distance (not an approximation) using the Maurer algorithm, which runs in linear time for images of any dimension.
It produces a signed distance map: pixels inside an object are given negative distances and pixels outside are given positive distances. By default inside is negative; enable Inside Is Positive to flip the convention. Unlike the Danielsson filters, this filter does not produce a Voronoi map.
Use this when you need an accurate signed Euclidean distance field — for example as a level-set input or for precise distance-from-boundary measurements.
Important — the default output is the squared distance, not the distance itself. Squared Distance is enabled by default so the filter can stay in fast integer arithmetic. To get true (un-squared) Euclidean distance, turn Squared Distance off.
Note: for true Euclidean distances, or when Use Image Spacing is enabled, choose a floating-point output array type. An integer output can only represent the squared, pixel-unit distances correctly.
Parameter Guidance
Inside Is Positive — by default, pixels inside an object are negative and pixels outside are positive. Enable this to reverse the convention.
Squared Distance — when enabled (the default), the output holds the distance squared. Disable it to get the actual (linear) distance.
Use Image Spacing — when enabled, distances are measured in the image’s physical units (using pixel spacing). When disabled, distances are measured in pixels.
Background Value — the input intensity that marks the background. Object pixels are everything else. Normally 0 (the default).
Required Input Sources
Requires a binary image where the Background Value marks background and all other pixels mark object. This is typically a segmentation mask produced upstream.
See Also
Reference: C. R. Maurer, Jr., R. Qi, and V. Raghavan, “A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2): 265-270, 2003.
Input Parameter(s)
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Inside Is Positive |
Bool |
Set if the inside represents positive values in the signed distance map. By convention ON pixels are treated as inside pixels. |
|
SquaredDistance |
Bool |
Set if the distance should be squared. |
|
Use Image Spacing |
Bool |
Set if image spacing should be used in computing distances. |
|
Background Value |
Scalar Value |
Float64 |
Set the background value which defines the object. Usually this value is = 0. |
Input Cell Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Image Geometry |
Geometry Selection |
Image |
Select the Image Geometry Group from the DataStructure. |
Input Cell Data |
Array Selection |
Allowed Types: int8, uint8, int16, uint16, int32, uint32, int64, uint64 |
The image data that will be processed by this filter. |
Output Cell Data
Parameter Name |
Parameter Type |
Parameter Notes |
Description |
|---|---|---|---|
Output Cell Data |
DataObjectName |
The result of the processing will be stored in this Data Array inside the same group as the input data. |
Example Pipelines
License & Copyright
Please see the description file distributed with this Plugin
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