When you click on ``Save results'', tnimage creates a file containing information about the grains.
The first part of the file is a list of data about each grain. The columns are: (1) the spot number; (2-3) the x and y coordinates of the center of each grain, (4) the size in pixels, (5) the total signal in the spot, and (6-9) the coordinates of the corners of the spot. If the image has been calibrated, the corners are also shown as their calibrated values (columns 10-13).
The second part of the file is a histogram of spot sizes. Each spot size, from 1 to whatever is the largest spot, is listed in the first column, followed by the number of spots of that particular size in column 2.
The third part of the file is a histogram of spot signals, combined into 256 bins. This would be the same data as in the Densitometry graph.
Recommended Threshold and Weight values for neural network method
| Type of pattern | Threshold | Match Wt. | Mismatch Wt. |
| Grains (monochrome) | 0.50 | 1.0 | -0.05 |
| Grains (color) | 0.45 | 1.0 | -0.10 |
| Grains (enhanced) | 0.55 | 1.0 | -0.15 |
| Faces | 0.60 | 1.0 | -0.15 |
| Faces (color) | 0.95 | 1.0 | -1.00 |
| Patterns | 0.90 | 1.0 | -0.50 |
Note: These values should be considered as starting points only. Different images will need different thresholds and mismatch weights.
Example of neural network grain counting:
Below is an example of neural network grain-counting of a moderately-difficult image illustrating some of the steps that may be necessary to obtain the most accurate counts. Panel A shows the original image, which contains several grains that are out of focus or clumped together, as well as some grains that are sharply focused, and a darker, out-of-focus blue cell in the center, which could be difficult to distinguish from the two faint grains on top of it. Under some conditions, the cell could even be misidentified as a large clump of grains. The image was converted to grayscale (B) and the grains were enhanced (by clicking on ``Enhance grains''), producing C, which is more easily analyzed. Using the default threshold value of 0.5 and Match and Mismatch weights of 1 and -0.05, however, caused a number of extraneous points to be counted as grains (D). This occurred because the filtering process also increased the noise slightly. Changing the mismatch weight to -0.15 solved the problem, and the program counted the number of grains as 64, the correct number (E). (Note: This figure may not display clearly in xdvi).
