<![CDATA[Accurate measurement of microbubble size is an essential step in evaluating the performance of corona-discharge-based ozone systems because bubble diameter distribution strongly influences interfacial area and mass transfer efficiency. This study aimed to develop a Python-based application for automatic microbubble diameter analysis using digital image segmentation. The program was developed by integrating OpenCV, NumPy, Pillow, Tkinter, and Matplotlib to support image loading, region-of-interest selection, preprocessing, thresholding, contour detection, visualization, and bubble diameter calculation. Size calibration was performed using a field-of-view approach based on camera sensor parameters and lens magnification to determine the pixel-to-micrometer conversion factor. Bubble diameter was calculated using the equivalent circular diameter derived from the segmented contour area. The developed application successfully distinguished bubble populations generated by two ozone diffusers, producing average diameters of 4.09 for the C-50 diffuser and 3.37 for the C-80 diffuser. These results demonstrate that the program can automatically detect microbubbles and provide systematic size distribution for laboratory-scale image analysis. The proposed application offers a practical, transparent, and open-source solution for rapid microbubble diameter measurement and has potential for broader application in gas–liquid bubble image analysis.]]>
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