Processing HSV Colored Medical Images and Adapting Color Thresholds for Computational Image Analysis: a Practical Introduction to an open-source tool
This tool addresses a practical bottleneck in medical image processing for researchers and clinicians using multi-center data, but it is incremental as it builds on existing preprocessing techniques.
The researchers tackled the problem of inconsistent color thresholds and clinical annotations in HSV-colored medical images, which hinder AI analysis, by developing an open-source MATLAB tool that successfully removes letters and adapts color thresholds.
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.