What is Box Counting?

"Box counting" is a sampling or data gathering process that FracLac uses to find several types of DF, in particular box counting dimension (D_Bs) and a feature known as lacunarity. The basic procedure is to systematically lay a series of grids of decreasing calibre (the boxes) over an image and record data (the counting) for each successive calibre. You can see what the basic task of laying successively sized grids looks like in the animation below; counting is usually a matter of counting how many of the boxes in each grid had any part of the important detail in the image in them. In the image shown here, the important detail is the white pixels, which are on the "unimportant" black background. If the boxes have stopped changing size, reload the page.

Calculating the Box Counting Dimension

Box counting solves the problem we identified on the previous page of our not usually knowing the relationship between scale and detail ahead of time when trying to find a scaling rule. The solution? Sample a pattern as we saw in the above illustration using arbitrary scaling, then make inferences using the handy-dandy technique of determining how detail (N) changes with scale (ε) by finding the slope of the logarithmic regression line for N and ε. That is, the D_B=the slope of ln N/ ln ε. Too much, too fast? Read on to fill in some blanks.

Scaling

You can see how this works by following the directions given in the box below, which illustrates how the number of boxes it takes to cover an image changes when the grid calibre changes.

Sampling to find the D_B

Below the small binary image of a diffusion limited aggregate is a list of grid calibres. The calibre is the size of the boxes in pixels, and the number, N, is the count of boxes that had foreground pixels in them. Hover your mouse over each of the choices in the list to see how changing grid calibre changes the count on a binary image.

As you noticed above, with each change in overall grid calibre, the area sampled by any box changed and so did the count . In general, for binary images, the detail or count that changes with scale (the size of the sampling element, the box, the grid calibre, etc.) is the number of boxes that had any foreground pixels in them. This is considered a proxy for the number of boxes required to cover the image or the number of parts in the image and is, for binary scans, what FracLac uses along with scale to calculate the D_B for the image.

It is critical to note here that, similar to what you discovered about scaling on the previous page when you compared the scaling in a line to the scaling in a Koch fractal, the change in count is not necessarily easy to predict from knowing the change in size or scale, ε. Fortunately, we do not have to do the actual counting, and can have our computers do the counting for any arbitrary scaling we set up. The image below graphs the relationship between ε and box count from the three grids you saw above, and shows the fractal dimension, 1.77, in the general equation for a scaling rule, found from the logarithmic regression line from this box counting data.

Other Sampling Issues

Mass

Whereas the discussion so far has probably told you all you need to know to understand how box counting can be used to find a fractal dimension, it has not touched on some points that I think might be very useful to know when you are doing box counting with FracLac. One important thing to note is that the number of pixels or "mass" in each box also changes with grid calibre, and that FracLac uses the mean mass, the average number of foreground pixels per box at any particular size, for calculating mass dimensions, lacunarity, and multifractality.

Grayscale Images

In addition, there is a special consideration for grayscale images. The discussion above was for binary images, but the "count" that changes for grayscale images is the average intensity of pixels per box. Thus, in grayscale scans, the sampling method changes, and FracLac calculates the grayscale fractal dimension or D_BGray from the relationship between the change in average intensity and the change in grid calibre. The image below illustrates the results of scanning and colour-coding local areas of a grayscale image according to the D_BGray using FracLac.

Grid Location

Another point you should know about box counting is that it is not just grid calibre, but also grid position, that can affect the actual count. You can see in the image here that even though the grid calibre and the foreground pixels are the same on both sides, the number of boxes needed to cover all of the foreground pixels (and therefore also the distribution of pixels counted) depends on where the grid is positioned.

In this example, it takes 12 green versus 14 yellow boxes to cover the same image with the same calibre of grid laid at different locations.

Because of this dependence on the sampling grid's orientation, FracLac uses multiple orientations of the same set of grid calibres to sample an image and delivers fractal dimensions based on different methods of transforming the data from multiple positions.

Sliding Scans

In addition to the "fixed grid" box counting we have discussed here so far, FracLac does "sliding box" scanning to calculate an image feature called sliding box lacunarity (which you might see as Sλ, SΛ, Slacunarity, SLAC, or Slac, all referring to variations of the one type of lacunarity).

The essential feature of a sliding scan is that one box moves over the image, overlapping itself at each slide. Thus, compared to a fixed scan, a sliding scan is considerably slower. The two animations below illustrate how a fixed scan can cover an entire image with multiple grid sizes before a sliding scan is done with even one box size. A couple of pictures say a couple of thousand words.

The figure below illustrates how all that sliding would look if we drew the box at each location and left it there. In the figure, the same box was laid over the image for the sliding scan (top of the figure) and the fixed scan (bottom), and traced for each location, to highlight the overlap in a sliding scan and the lack of it in the fixed. When considering this notion, it is important to note that the distances slid horizontally and vertically are options the user sets in FracLac.

There is, accordingly, a notable difference in sampling between fixed and sliding scans. In the regular fixed scan, each part of the image is sampled only once by any particular box size in a series of grid calibres, but in the sliding scan, parts of the image can be resampled multiple times by one box size.

When many calibres of grids are used, the difference in sampling becomes even more meaningful. To delve into this concept, look at the image shown here of a scaled version of an 8-segment quadric fractal (D_F=1.50) along with a part of it zoomed in. The zoomed part is there so you can see the detail as we look at how sampling is different between fixed and sliding scans using a series of grid sizes.

Below are pairs of grid images made from the original. The left side of each pair is made from a sliding scan and the right from a fixed scan of the orginal image. The boxes that contained foreground pixels for each type of scan at the same grid calibre are drawn in cyan on each image in the pair. If you hover your mouse over the different items in the list below the pairs, you will see how both grid calibre and scan type affect the number of boxes. Two of the images show the part corresponding to the zoomed in part above, to illustrate the difference in greater detail.

Re-sampling part of an image in sliding scans dramatically increases the time to scan. It also affects the data gathered, especially as box size increases. Accordingly, rather than the number of boxes, the average pixels per box is used in the calculations from overlapping scans.

tutorial on how to generate grid images in FracLac.
Lacunarity tutorial
Sliding Box Lacunarity Scan Settings

Local Connected Set

Another variation in box counting with FracLac is in sampling for the local connected fractal dimension (LCFD). FracLac calculates the LCFD using a technique applied only to binary images, whereby they are sampled pixel by pixel in terms of the local connected set around each pixel, an example of which is shown in the image below.

The basic rule for finding the local connected set is that all foreground pixels that are in the 8x8 environment of a seed pixel are considered connected, and this basic rule is applied to find the connected set for some predetermined arbitrary distance around that starting pixel (the arbitrary distance is a user setting).

Then, in turn, the rule for data gathering is based on this connected set rather than the image at large. The actual data gathering uses fixed boxes, but not in the same way as for a standard box count. Rather, as is illustrated in the figure below, a series of changing box calibres is centred, one by one, on a starting pixel, and the number of pixels that were in the connected set is counted for each calibre. Because they are centred on a pixel, the box sizes in a LCFD scan should be odd numbers. There is also an option to use circles as shown in the figure, which was, by the way, generated as an optional part of the LCFD scan. With this method of sampling, the count of boxes at any size is always 1, so the mass of pixels rather than the count of boxes is used in the regression equation to determine the LCFD.

An important part of LCFD analysis is the potential for visual presentation (see Landini). What you see in the images here is one binary image scanned once, but colour-coded multiple times to highlight different types of local variations as can be applied in diagnostics, for instance, where "hot spots" need to be detected and located, with the same area being differentiated for multiple features. This is discussed further in the LCFD Analysis page

How to Set up and Interpret a LCFD Scan
How to Calculate the LCFD

Sub Scans and Block Scans

FracLac calculates local D_Bs for subareas of an image. In these cases, the sample is different to start with - rather than scanning the image as a whole, parts are analyzed individually. But the actual scanning method is otherwise the same as for regular fixed grid box counting.

There are two types of sub scans that you can do in FracLac. In the first, portions of an image are automatically selected using ImageJ's particle analyzer. The image above, for instance, shows the results of a single scan of an image containing multiple cells, where each cell was automatically selected, analyzed, and displayed colour-coded according to its calculated D_B.

In the second sub scan type, images are divided into blocks. The image blocks are selected systematically or randomly, depending on the user's choices. The image to the right illustrates one type of graphic generated from a rectangular subareas scan. For further discussion, see the glossary and Sub scans page.

FracLac also does block texture analyses for sliding box lacunarity and standard box counts.

Sub Scan Options and Block Texture Analysis.