logarithmic graphs for each suture analyzed in both of the methods and all of them
clearly showed a linear relationship. This suggests that these sutures are self-similar and
therefore, by definition, are fractal.
Unfortunately this still leaves the problem of trying to provide an explanation for
why the ruler and information fractal dimensions are not demonstrating equivalence like
they should. One possibility is that due to the complicated mathematics that are
introduced into the information dimension in order to weight the boxes, the equivalence
that exists between the box and ruler dimension is lost. To test this theory, fractal
analysis was conducted again on the same sutures using the box dimension (Tables 9 and
10). A simple regression was conducted and as Figure 9 demonstrates there is still no
linear relationship (r-squared 0.0804). This does not support the idea that the more in
depth mathematical calculations affected the equivalence. The reason for this may be
that the number of points collected could affect the outcome of the fractal dimension.
This implies that these different methods of fractal analysis are not measuring
complexity in the same fashion. Uncertainty exists as to which method is more
appropriate for analyzing human cranial and facial sutures, but one insight gained is that
these methods are not equivalent. This means more testing (e.g.) needs to be completed
in order to try to determine which method is more accurate. Besides the type of dataset
utilized, another factor that may affect which method is better is how the data is
collected. In other words, it may be that both methods are appropriate for analyzing
human sutures, but depending on the method used to extract the suture from the specimen
and manipulate it so it can be imported into this software, one method may prevail over