  Background: Using artificial intelligence (AI) techniques for computational
medical image analysis has shown promising results. However, colored images are
often not readily available for AI analysis because of different coloring
thresholds used across centers and physicians as well as the removal of
clinical annotations. We aimed to develop an open-source tool that can adapt
different color thresholds of HSV-colored medical images and remove annotations
with a simple click.
  Materials and Methods: We built a function using MATLAB and used multi-center
international shear wave elastography data (NCT 02638935) to test the function.
We provide step-by-step instructions with accompanying code lines.
  Results: We demonstrate that the newly developed pre-processing function
successfully removed letters and adapted different color thresholds of
HSV-colored medical images.
  Conclusion: We developed an open-source tool for removing letters and
adapting different color thresholds in HSV-colored medical images. We hope this
contributes to advancing medical image processing for developing robust
computational imaging algorithms using diverse multi-center big data. The
open-source Matlab tool is available at
https://github.com/cailiemed/image-threshold-adapting.
