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Different combinations of input parameters to filament identificationalgorithms, such as Disperse and FilFinder, produce numerous different outputskeletons. The skeletons are a one pixel wide representation of the filamentarystructure in the original input image. However, these output skeletons may notnecessarily be a good representation of that structure. Furthermore, a givenskeleton may not be as good a representation as another. Previously there hasbeen no mathematical `goodness-of-fit' measure to compare output skeletons tothe input image. Thus far this has been assessed visually, introducing visualbias. We propose the application of the mean structural similarity index(MSSIM) as a mathematical goodness-of-fit measure. We describe the use of theMSSIM to find the output skeletons most mathematically similar to the originalinput image (the optimum, or `best', skeletons) for a given algorithm, andindependently of the algorithm. This measure makes possible systematicparameter studies, aimed at finding the subset of input parameter valuesreturning optimum skeletons. It can also be applied to the output ofnon-skeleton based filament identification algorithms, such as the Hessianmatrix method. The MSSIM removes the need to visually examine thousands ofoutput skeletons, and eliminates the visual bias, subjectivity, and limitedreproducibility inherent in that process, representing a major improvement onexisting techniques. Importantly, it also allows further automation in thepost-processing of output skeletons, which is crucial in this era of `bigdata'.

Removing visual bias in filament identification: a new goodness-of-fit measure