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.

翻译：背景：利用人工智能（AI）技术进行医学图像计算分析已显示出良好的前景。然而，由于不同中心和医生采用不同的颜色阈值，以及临床注释的移除，彩色图像往往无法直接用于AI分析。我们旨在开发一个开源工具，该工具可适配HSV彩色医学图像的不同颜色阈值，并通过简单点击移除注释。材料与方法：我们使用MATLAB构建了一个函数，并利用多中心国际剪切波弹性成像数据（NCT 02638935）对该函数进行测试。我们提供了包含代码行的逐步说明。结果：我们证明，新开发的预处理函数成功移除了字母，并适配了HSV彩色医学图像的不同颜色阈值。结论：我们开发了一个开源工具，用于移除HSV彩色医学图像中的字母并适配不同颜色阈值。我们希望这有助于推进医学图像处理，为利用多样化多中心大数据开发稳健的计算成像算法做出贡献。该开源MATLAB工具可在https://github.com/cailiemed/image-threshold-adapting获取。