Well, difficult to answer without knowing what "as described" means and without an image...

But if your thresholds are both at 255, no wonder IJ does not like it : can't have both at the same value.

The error message you encountered in ImageJ, "the threshold may not be correct (255-255)," typically occurs when there's an issue with the threshold settings during image analysis. It indicates that ImageJ is not able to determine a valid threshold for segmenting objects in your image. Here are some steps to help you troubleshoot and resolve this issue:

1. **Check Image Consistency**:

- Ensure that the image you're trying to analyze is consistent and well-prepared. Any unexpected changes in the image, such as variations in brightness or contrast, can affect thresholding.

2. **Review Threshold Settings**:

- Double-check the threshold settings you're using. In ImageJ, you can access the Threshold tool by going to `Image > Adjust > Threshold`. Make sure that the thresholding method (e.g., Auto, Manual, or a specific algorithm) and the lower and upper threshold values are set correctly for your image.

3. **Image Preprocessing**:

- It's good practice to preprocess your image before thresholding. You mentioned that you adjusted the contrast and applied a Gaussian filter, which is a common preprocessing step. Ensure that these adjustments are appropriate for your image.

4. **Image Format**:

- Verify that your image is in a supported format and that it's not corrupted. ImageJ works with common image formats like JPEG, PNG, TIFF, etc.

5. **Image Calibration**:

- If your image is in scientific units (e.g., micrometers per pixel), make sure to set the appropriate scale and calibration settings in ImageJ to ensure accurate measurements.

6. **Update or Reinstall ImageJ**:

- Sometimes, software issues can be resolved by updating to the latest version of ImageJ or reinstalling it if you suspect any corruption in the installation.

7. **Memory and System Resources**:

- Ensure that your computer has sufficient memory and system resources to process the image. Large images may require more memory.

8. **Check for Outliers**:

- High-intensity outliers or artifacts in your image can impact thresholding. Inspect the image for such anomalies and consider removing or correcting them.

9. **Sample Size and Image Variability**:

- Ensure that you have a representative sample size of objects in your image. Very small or very large samples can lead to thresholding issues. Additionally, if the objects vary greatly in intensity or size, this can affect thresholding.

10. **Manual Thresholding**:

- If automated thresholding methods fail, you can try manual thresholding by selecting a suitable threshold value visually.

11. **Consult ImageJ Community**:

- If you've tried these steps and still encounter the issue, consider reaching out to the ImageJ community through forums or mailing lists. Other users may have encountered similar problems and can provide guidance.