Changing the Bin Size affects the resolution of the histogram in the Plots tab and the histogram table in the Report tab. A too large Bin Size leads to a “blocky” histogram, while a too small Bin Size leads to a volatile and noisy histogram. The Bin Size should be chosen so that the histogram is smooth and detailed. The effect of the Bin Size on the histogram is illustrated below.

You can classify the grains in the structure into different types based on the estimated diameter.

To determine the threshold value, three Thresholding Methods are available:

• k-Means: uses the k-Means algorithm to find thresholds for the chosen Number of Enter value Types.

• Otsu: uses the Otsu algorithm to find thresholds for the chosen Number of Enter value Types.
• Manual: define your own thresholds by writing a comma-separated list of thresholds into Threshold(s). You implicitly define the number of pore types by the number of thresholds you enter, e.g, if you enter two threshold values this will result in three pore types.

After clicking Apply, the Grain Type Analysis table is updated and shows for each type the relevant parameters. In the Plot tab, the Gauss fit of each grain type and the threshold values are added to the histogram plot. This allows you to control if the computed thresholds fit to the grain diameter distribution or need to be adapted.

Based on these diameter classes, the structure is automatically segmented and can be loaded from the Data Visualization tab