Multi-Label 3D Anisotropic Euclidean Distance Transform (MLAEDT-3D). Useful for dense biomedical image segmentations.
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Multi-Label Anisotropic 3D Euclidean Distance Transform (MLAEDT-3D)

Compute the Euclidean Distance Transform of a 1d, 2d, or 3d labeled image containing multiple labels in a single pass with support for anisotropic dimensions. Curious what the output looks like? Download the movie!

Use Cases

  1. Compute the distance transform of a volume containing multiple labels simultaneously and then query it using a fast masking operator.
  2. Convert the multi-label volume into a binary image (i.e. a single label) using a masking operator and compute the ordinary distance transform.

Python Installation

Requires a C++ compiler.

pip install numpy
pip install edt

Python Usage

Consult help(edt) after importing. The edt module contains: edt and edtsq which compute the euclidean and squared euclidean distance respectively. Both functions select dimension based on the shape of the numpy array fed to them. 1D, 2D, and 3D volumes are supported. 1D processing is extremely fast. Numpy boolean arrays are handled specially for faster processing.

If for some reason you'd like to use a specific 'D' function, edt1d, edt1dsq, edt2d, edt2dsq, edt3d, and edt3dsq are available.

The two optional parameters are anisotropy and black_border. Anisotropy is used to correct for distortions in voxel space, e.g. if X and Y were acquired with a microscope, but the Z axis was cut more corsely.

black_border allows you to specify that the edges of the image should be considered in computing pixel distances (it's also slightly faster).

import edt
import numpy as np

labels = np.ones(shape=(512, 512, 512), dtype=np.uint32)
dt = edt.edt(labels, anisotropy=(6, 6, 30), black_border=True) # e.g. for the S1 dataset by Kasthuri et al., 2014

C++ Usage

Include the edt.hpp header which includes the implementation in namespace edt.

#include "edt.hpp"

int main () {

    int* labels = new int[3*3*3]();

    int sx = 3, sy = 3, sz = 3;
    float wx = 6, wy = 6, wz = 30; // anisotropy

    float* dt = edt::edt<int>(labels, sx, sy, sz, wx, wy, wz);

    return 0;

For compilation, I recommend the compiler flags -O3 and -ffast-math.


The connectomics field commonly generates very large densely labeled volumes of neural tissue. Some algorithms, such as the TEASAR skeletonization algorithm [1] and its descendant [2] require the computation of a 3D Euclidean Distance Transform (EDT). We found that the scipy implementation of the distance transform (based on the Voronoi method of Maurer et al. [3]) was too slow for our needs despite being relatively speedy. It also lacked support for anisotropic images.

A Labeled 3D Image. Credit: Kisuk Lee
Fig. 1: A labeled 3D connectomics volume. Credit: Kisuk Lee

The connectomics field commonly generates very large densely labeled volumes of neural tissue. Some algorithms, such as the TEASAR skeletonization algorithm [1] and its descendant [2] require the computation of a 3D Euclidean Distance Transform (EDT). We found that the scipy implementation of the distance transform (based on the Voronoi method of Maurer et al. [3]) was too slow for our needs despite being relatively speedy. It also lacked support for anisotropic images.

The scipy EDT took about 20 seconds to compute the transform of a 512x512x512 voxel binary image. Unfortunately, there are typically more than 300 distinct labels within a volume, requiring the serial application of the EDT. While cropping to the ROI does help, many ROIs are diagonally oriented and span the volume, requiring a full EDT. I found that in our numpy/scipy based implementation of TEASAR, EDT was taking approximately a quarter of the time on its own. The amount of time the algorithm spent per a block was estimated to be multiple hours per a core.

It's common for connectomics datasets to have anisotropic ratios of 10:1 (e.g. 4nm x 4nm x 40nm resolution image stacks), making this treatment important for our use of TEASAR. scipy.ndimage.distance_transform_edt did not support weighted dimensions, though in the discussion section Maurer et al is clear that their algorithm supports anisotropy.

I realized that it's possible to compute the EDT much more quickly by computing the distance transform in one pass by making it label boundary aware at slightly higher computational cost. Since the distance transform does not result in overlapping boundaries, it is trivial to then perform a fast masking operation to query the block for the appropriate distance transform.

The implementation presented here uses concepts from the 1994 paper by T. Saito and J. Toriwaki [4] and uses a linear sweeping method inspired by the 1966 method of Rosenfeld and Pfaltz [4] and of Mejister et al [7] with that of Mejister et al's and Felzenszwald and Huttenlocher's 2012 [6] two pass linear time parabolic minmal envelope method. I incorporate a few minor modifications to the algorithms to remove the necessity of a black border. My own contribution here is the modification of both the linear sweep and linear parabolic methods to account for multiple label boundaries.

This implementation was able to compute the distance transform of a binary image in 7-8 seconds. When adding in the multiple boundary modification, this rose to 9 seconds. Incorporating the cost of querying the distance transformed block with masking operators, the time for all operators rose to about 90 seconds, well short of the over an hour required to compute 300 passes.

Basic EDT Algorithm Description

A naive implementation of the distance transform is very expensive as it would require a search that is O(N2) in the number of voxels. In 1994, Saito and Toriwaki (ST) showed how to decompose this search into passes along x, y, and z linear in the number of voxels. After the X-axis EDT is computed, the Y-axis EDT can be computed on top of it by finding the minimum x2 + y2 for each voxel within each column. You can extend this argument to N dimensions. The pertient issue is then finding the minima efficiently without having to scan each column a quadratic number of times.

Felzenszwalb and Huttenlocher (FH) [6] and others have described taking advantage of the geometric interpretation of the distance function, as a parabola. Using ST's decomposition of the EDT into three passes, each broken up by row, the problem becomes one dimensional. FH described computing each pass on each row by finding the minimal envelope of the space created by the parabolas that project from the vertices located at (i, f(i)) for a one dimensional image f.

This method works by first scanning the row and finding a set of parabolas that constitue the lower envelope. Simultaneously during this linear scan, it computes the abscissa of the nearest parabola to the left and thereby defines the effective domain of each vertex. This linear reading scan is followed by a linear writing scan that records the height of the envelope at each voxel.

This method is linear and relatively fast, but there's another trick we can do to speed things up. The first transformation is special as we have to change the binary image f into a floating point representation. FH recommended using an indicator function that records zeros for out of set and infinities for within set voxels on the first pass. However, this is somewhat cumbersome to reason about and requires an additional remapping of the image.

The original Rosenfeld and Pfaltz (RP) paper [5] demonstrated a remarkably simple two pass sweeping algorithm for computing the manhattan distance (L1 norm, visualized here). On the first pass, the L1 and the L2 norm agree as only a single dimension is involved. Using very simple operators and sweeping forward we can compute the increasing distance of a voxel from its leftmost bound. We can then reconcile the errors in a backward pass that computes the minimum of the results of the first pass and the distance from the right boundary. Mejister, Roerdink, and Hesselink (MRH) [7] described a similar technique for use with binary images within the framework set by ST.

In all, we manage to achieve an EDT in six scans of an image in three directions. The use of the RP and MRH inspired method for the first transformation saves about 30% of the time as it appears to use a single digit percentage of the CPU time. In the second and third passes, due to the read and write sequence of FH's method, we can read and write to the same block of memory, increasing cache coherence and reducing memory usage.

Multi-Label 1D RP and MRH Inspired Algorithm

The forward sweep looks like:

f(a_i) = 0               ; a_i = 0  
       = a_i + 1         ; a_i = 1, i > 0
       = inf             ; a_i = 1, i = 0

I modify this to include consideration of multi-labels as follows:

let a_i be the EDT value at i
let l_i be the label at i (seperate 1:1 corresponding image)
let w be the anisotropy value

f(a_i, l_i) = 0          ; l_i = 0
f(a_i, l_i) = a_i + w    ; l_i = l_i-1, l_i != 0

f(a_i, l_i) = w          ; l_i != l_i-1, l_i != 0 
  f(a_i-1, l_i-1) = w    ; l_i-1 != 0
  f(a_i-1, l_i-1) = 0    ; l_i-1 = 0

The backwards pass is unchanged:

from n-2 to 1:
    f(a_i) = min(a_i, a_i+1 + 1)
from 0 to n-1:
    f(a_i) = f(a_i)^2

Anisotropy Support in FH Algorithm

A small update to the parabolic intercept equation is necessary to properly support anisotropic dimensions. The original equation appears on page 419 of their paper (reproduced here):

s = ((f(r) + r^2) - (f(q) + q^2)) / 2(r - q)

Where given parabola on an XY plane:
s:    x-coord of the intercept between two parabolas
r:    x-coord of the first parabola's vertex
f(r): y-coord of the first parabola's vertex
q:    x-coord of the second parabola's vertex
f(q): y-coord of the second parabola's vertex

However, this equation doesn't work with non-unitary anisotropy. The derivation of the proper equation follows.

1. Let w = the anisotropic weight
2. (ws - wq)^2 + f(q) = (ws - wr)^2 + f(r)
3. w^2(s - q)^2 + f(q) = w^2(s - r)^2 # w^2 can be pulled out after expansion
4. w^2( (s-q)^2 - (s-r)^2 ) = f(r) - f(q)
5. w^2( s^2 - 2sq + q^2 - s^2 + 2sr - r^2 ) = f(r) - f(q)
6. w^2( 2sr - 2sq + q^2 - r^2 ) = f(r) - f(q)
7. 2sw^2(r-q) + w^2(q^2 - r^2) = f(r) - f(q)
8. 2sw^2(r-q) = f(r) - f(q) - w^2(q^2 - r^2)

=> s = (w^2(r^2 - q^2) + f(r) - f(q)) / 2w^2(r-q)

Multi-Label Felzenszwalb and Huttenlocher Variation

The parabola method attempts to find the lower envelope of the parabolas described by vertices (i, f(i)).

We handle multiple labels by running the FH method on contiguous blocks of labels independently. For example, in the following column:

Y LABELS:  0 1 1 1 1 1 2 0 3 3 3 3 2 2 1 2 3 0
X AXIS:    0 9 9 1 2 9 7 0 2 3 9 1 1 1 4 4 2 0
FH Domain: 1 1 1 1 1 1 2 2 3 3 3 3 4 4 5 6 7 7

Each domain is processed within the envelope described below. This ensures that edges are labeled 1. Alternatively, one can preprocess the image and set differing label pixels to zero and run the FH method without changes, however, this causes edge pixels to be labeled 0 instead of 1. I felt it was nicer to let the background value be zero rather than something like -1 or infinity since 0 is more commonly used in our processes as background.

Additional Parabolic Envelope

The methods of RP, ST, and FH all appear to depend on the existence of a black border ringing the ROI. Without it, the Y pass can't propogate a min signal near the border. However, this approach seems wasteful as it requires about 6s2 additional memory (for a cube) and potentially a copy into bordered memory. Instead, I opted to impose an envelope around all passes. For an image I with n voxels, I implicitly place a vertex at (-1, 0) and at (n, 0). This envelope propogates the edge effect through the volume.

To modify the first X-axis pass, I simply mark I[0] = 1 and I[n-1] = 1. The parabolic method is a little trickier to modify because it uses the vertex location to reference I[i]. -1 and n are both off the ends of the array and therefore would crash the program. Instead, I add the following lines right after the second pass write:

// let w be anisotropy
// let i be the index
// let v be the abcissa of the parabola's vertex
// let n be the number of voxels in this row
// let d be the transformed (destination) image

d[i] = square(w * (i - v)) + I[v] // line 18, pp. 420 of FH [6]
envelope = min(square(w * (i+1)), square(w * (n - i))) // envelope computation
d[i] = min(envelope, d[i]) // application of envelope

These additional lines add about 3% to the running time compared to a program without them. I have not yet made a comparison to a bordered variant.

Side Notes on Further Performance Improvements

If this scheme seems good to you and you're working with binary images, it is possible to reduce memory usage down to 1x (down from 2x) by reusing the input volume. Additionally, it is possible to convert to nearly all integer operations when using the squared distance. Outputing the real euclidean distance requires either fixed or floating decimal points.

The most expensive operation appears to be the Z scan of our 512x512x512 float cubes. This is almost certainly because of L1 cache misses. The X scan has a stride of 4 bytes and is very fast. The Y scan has a stride of 512*4 bytes (2 KiB), and is also very fast. The Z scan has a stride of 512*512*4 bytes (1 MiB) and seems to add 3-5 seconds to the running time. The L1 cache on the tested computer is 32kB. I considered using half precision floats, but that would only bring it down to 512KiB, which is still a cache miss.

For our use case, this EDT implementation is probably fast enough. I have found that for 5123 voxels, the dominant factor now is by far the fast masking operation (81 out of 90 seconds for 300 labels). However, there is a class of algorithms by Hongkai Zhao [8][9] called the "fast sweep" method for Eikonal equations (i.e. superset of boundary distance equations) that appear to reduce the number of sweeps from two per a dimension to one. Therefore, I expect that an implementation of fast sweep would improve the EDT by a factor of about 2.


  1. M. Sato, I. Bitter, M.A. Bender, A.E. Kaufman, and M. Nakajima. "TEASAR: Tree-structure Extraction Algorithm for Accurate and Robust Skeletons". Proc. 8th Pacific Conf. on Computer Graphics and Applications. Oct. 2000. doi: 10.1109/PCCGA.2000.883951 (link)
  2. I. Bitter, A.E. Kaufman, and M. Sato. "Penalized-distance volumetric skeleton algorithm". IEEE Transactions on Visualization and Computer Graphics Vol. 7, Iss. 3, Jul-Sep 2001. doi: 10.1109/2945.942688 (link)
  3. C. Maurer, 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. Vol. 25, No. 2. February 2003. doi: 10.1109/TPAMI.2003.1177156 (link)
  4. T. Saito and J. Toriwaki. "New Algorithms for Euclidean Distance Transformation of an n-Dimensional Digitized Picture with Applications". Pattern Recognition, Vol. 27, Iss. 11, Nov. 1994, Pg. 1551-1565. doi: 10.1016/0031-3203(94)90133-3 (link)
  5. A. Rosenfeld and J. Pfaltz. "Sequential Operations in Digital Picture Processing". Journal of the ACM. Vol. 13, Issue 4, Oct. 1966, Pg. 471-494. doi: 10.1145/321356.321357 (link)
  6. P. Felzenszwald and D. Huttenlocher. "Distance Transforms of Sampled Functions". Theory of Computing, Vol. 8, 2012, Pg. 415-428. doi: 10.4086/toc.2012.v008a019 (link)
  7. A. Meijster, J.B.T.M. Roerdink, and W.H. Hesselink. (2002) "A General Algorithm for Computing Distance Transforms in Linear Time". In: Goutsias J., Vincent L., Bloomberg D.S. (eds) Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol 18. Springer, Boston, MA. doi: 10.1007/0-306-47025-X_36 (link)
  8. H. Zhao. "A Fast Sweeping Method for Eikonal Equations". Mathematics of Computation. Vol. 74, Num. 250, Pg. 603-627. May 2004. doi: 10.1090/S0025-5718-04-01678-3 (link)
  9. H. Zhao. "Parallel Implementations of the Fast Sweeping Method". Journal of Computational Mathematics. Vol. 25, No.4, Pg. 421-429. July 2007. Institute of Computational Mathematics and Scientific/Engineering Computing. (link)