# Measuring fractal dimension by box-counting¶

## Theory¶

The term fractal dimension was introduced by Benoit Mandelbrot in 1967 to explain self-similarity of a pattern. A fractal dimension is defined as a ratio of the change in detail to the change in scale. It is used as an index that quantifies the complexity of a fractal pattern.

Famously, fractal dimensions have been used to analyze the length of the British coastline. A coastline’s measured length is observed to change depending on the length of the measuring stick used. In 2-D and 3-D, this notion can be extended to the length of a measuring pixel or voxel, respectively.

### Box Counting Method¶

One way to determine fractal dimension of an image is the box counting method. Boxes of various sizes are laid over the image in a fixed grid pattern. The number of boxes that span the edge of the pattern (i.e. partially 1 and partially 0) are tallied as a function of box size. This count is then used to calculate the fractal dimension .

## Mathematical Definition¶

The relationship of a pattern’s fractal dimension and its measuring element can be expressed as:

$N \propto \frac{1}{D^F}$
$F = \lim_{D \to 0} \frac{-log N(D)}{log(D)}$

where:

• N: number of boxes of side D that span an edge

• D: size of the boxes

• F: fractal dimension

## Example¶

A Sierpinski carpet has a known fractal dimension of 1.8928. Performing the box counting method found its fractal dimension as approximately 1.8 ~ 1.9.

First, import the needed packages.

import matplotlib.pyplot as plt
import porespy as ps
ps.visualization.set_mpl_style()

[03:07:32] ERROR    PARDISO solver not installed, run pip install pypardiso. Otherwise,          _workspace.py:56
simulations will be slow. Apple M chips not supported.


Generate a sierpinski carpet and visualize.

im = ps.generators.sierpinski_foam(4, 5)
plt.imshow(im);


Finally, apply the box count function and visualize.

data = ps.metrics.boxcount(im)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.set_yscale('log')
ax1.set_xscale('log')
ax1.set_xlabel('box edge length')
ax1.set_ylabel('number of boxes spanning phases')
ax2.set_xlabel('box edge length')
ax2.set_ylabel('slope')
ax2.set_xscale('log')
ax1.plot(data.size, data.count,'-o')
ax2.plot(data.size, data.slope,'-o');


The horizontal portion of the slope vs box edge length curve, between $$10^1$$ and $$10^2$$ is a flat line with a value of approximately 1.9. Beyond a box edge length of $$10^2$$ the analysis becomes impacted by the finite image size so the result begins to diverge.