Distance transforms module

#include "diplib/distance.h"

void dip::EuclideanDistanceTransform( dip::Image const& in, dip::Image& out, dip::String const& border = S::BACKGROUND, dip::String const& method = S::SEPARABLE)

Euclidean distance transform

This function computes the Euclidean distance transform with one of several different methods. The distance transform out indicates the distances from the objects (binary 1’s) to the nearest background (binary 0’s) of in. out is of type dip::DT_SFLOAT, and is zero everywhere where in is zero.

Computed distances use the pixel sizes (ignoring any units). To compute distances in pixels, reset the pixel size (dip::Image::ResetPixelSize). Note that, when pixels sizes are correctly set, this function handles anisotropic sampling densities correctly.

The border parameter specifies whether the edge of the image should be treated as objects ("object") or as background ("background").

The method parameter specifies the method to use to compute the distances. There are three general algorithms implemented:

1. A separable algorithm based on parabolic erosions as first described by van den Boomgaard (1992) and later by Meijster et al. (2002), but for some reason frequently referred to as “Felzenszwalb and Huttenlocher”, who published the same method without attribution 10 years later. This is a very fast algorithm of linear time complexity, it is parallelized, and produces exact Euclidean distances in any number of dimensions. method must be "separable", which is the default, or "square", in which case squared distances are returned.

2. A vector distance transform, which propagates vectors to the nearest background pixel instead of propagating distances as the chamfer method does. This leads to a fairly fast algorithm that can yield exact results. This algorithm was described by Mullikin, based on previous work by Danielsson and Ye.

   method must be one of "fast", "ties" or "true". The difference is in how vectors of equal length are propagated. The "fast" method chooses one of them, which can lead to errors of around 0.2 pixels. The "ties" method stores all vectors of equal length, which reduces the average errors but can still lead to significant errors. The "true" method stores also vectors close in length, which leads to exact distances in all cases. These three methods are listed in increasing computational complexity.

   This method works with 2D and 3D images only. The "fast" method can have a time advantage over the "separable" method in situations where parallelism is not an option. When exact distances are needed, the separable algorithm is always fastest.

   Individual vector components of the Euclidean distance transform can be obtained with dip::VectorDistanceTransform.

3. A brute force algorithm that scales quadratically with the number of pixels. The results are always exact. Use only with small images to determine a ground-truth result. For 2D and 3D inputs only. method must be "brute force".

void dip::VectorDistanceTransform( dip::Image const& in, dip::Image& out, dip::String const& border = S::BACKGROUND, dip::String const& method = S::FAST)

Euclidean vector distance transform

This function produces the vector components of the Euclidean distance transform, in the form of a vector image. The norm of out is identical to the result of dip::EuclideanDistanceTransform.

See dip::EuclideanDistanceTransform for detailed information about the parameters. Valid method strings are "fast", "ties", "true" and "brute force". That is, "separable" or "square" are not allowed.

in should not have any dimension larger than 10⁷ pixels, otherwise the vector components will underflow.

void dip::GreyWeightedDistanceTransform( dip::Image const& grey, dip::Image const& bin, dip::Image const& mask, dip::Image& out, dip::Metric metric = {S::CHAMFER,2}, dip::String const& mode = S::FASTMARCHING)

Grey-weighted distance transform

dip::GreyWeightedDistanceTransform determines the grey weighted distance transform of the object elements in the bin image, using the sample values in grey as the local weights for the distances. That is, it computes the integral of grey along a path from each pixel that is set in bin (foreground) to any pixel that is not set in bin (background), with the path chosen such that this integral is minimal.

The images bin and grey must have the same sizes. bin is a binary image, grey is real-valued, and both must be scalar. out will have type dip::DT_SFLOAT. If grey is not forged, it is assumed to be valued 1 everywhere.

mask is an optional input that further constrains the paths taken by the distance transform. Paths only go through those pixels that are set in mask. Pixels not set in mask will have either a value of 0 or infinity in the output, depending on whether bin was set or not. If mask is not forged, paths are not constrained. If mask is forged, it must be of the same sizes as bin and grey, and be binary and scalar.

Note that if both grey and mask are not forged, the output will be an approximation to the Euclidean distance transform. Prefer to use dip::EuclideanDistanceTransform in this case, as it’s much faster and can produce exact Euclidean distances.

The output distances are computed taking pixel sizes into account (ignoring any units). This means that this function will correctly handle anisotropic sampling densities. Pixel sizes are taken from grey, and if it doesn’t have pixel sizes, they are taken from bin. To compute distances in pixels, reset the pixel size (dip::Image::ResetPixelSize) of both images.

This function uses one of two algorithms: the fast marching algorithm (Sethian, 1996), or a simpler propagation algorithm that uses a chamfer metric (after work by Verwer and Strasters). metric is used only in the latter case. mode selects the algorithm used and what output is produced:

• "fast marching" uses the fast marching algorithm. This is the default.
• "chamfer" uses the chamfer metric algorithm.
• "length" also uses the chamfer metric algorithm, but outputs the length of the optimal path, rather than the integral along the path.

The fast marching algorithm approximates Euclidean distances. It yields the most isotropic result, though it is biased.

The chamfer metric is defined by the parameter metric. Any metric can be given, but dip::Metric("chamfer", 1) or dip::Metric("chamfer", 2) are to be preferred, as they produce unbiased distances (with octagonal and dodecagonal unit circles, respectively). See dip::Metric for more information. If the metric has a pixel size set, it will overrule any pixel sizes in grey and bin. The chamfer metric algorithm is a little faster than the fast marching algorithm, with smaller neighborhoods being faster than larger neighborhoods.

void dip::GeodesicDistanceTransform( dip::Image const& marker, dip::Image const& condition, dip::Image& out)

Geodesic distance transform

This function computes the geodesic distance transform of the object elements in the marker image, with paths constrained to the condition image. That is, for each set pixel in marker, the distance to the background in marker is computed, along a path that stays completely within set pixels of condition. Pixels where marker or condition are not set, the output will not be set. Specifically, if a pixel is set in marker but not in condition, then that pixel will have a value of 0 in the output, but this value will not be used as a seed for paths, so that its neighbors can have a large distance value.

Non-isotropic pixel sizes are supported. The pixel sizes of marker are used, those of condition are ignored.

The images marker and condition must have the same sizes, and be scalar and binary. out will have type dip::DT_SFLOAT.

This function is currently implemented in terms of dip::GreyWeightedDistanceTransform, see that function for literature and implementation details. It uses the fast marching algorithm to produce a reasonable approximation of Euclidean distances.