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This study presents an innovative project for the quantification and morphological characterization of PC12 cell cultures, offering a comparative analysis of two methods: ImageJ software and a newly developed Python algorithm. Utilizing images captured at specific intervals on days 1, 3, and 5, both methods involved preprocessing steps such as brightness and contrast adjustment, segmentation, and morphological operations, followed by particle analysis for quantification. The results showed no significant differences between the methods in measuring the area occupied by the cells and cluster sizes on the same day. However, both methods revealed statistically significant increases in cell proliferation between days. The Python algorithm, built with libraries such as OpenCV and Numpy, offers additional advantages, including automation, a graphical user interface (GUI) created in Visual Studio Code and easy data export to Excel. These features make it a feasible and effective tool for real-time cell analysis, complementing and potentially enhancing traditional methods. The study concludes that the proposed system is as accurate and efficient as ImageJ, with added operational flexibility, providing a robust alternative for quantitative cell culture analysis. This advancement holds significant promise for applications in tissue engineering and regenerative medicine.

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Design and Implementation of a System for Morphological Characterization of Cells Using Image Processing with Python

  • 会议论文

  • 发表日期：

    2025年

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  • Anthony A. Hurtado Escobar,
  • Bernardino Denis,
  • Rolando A. Gittens,
  • Ernesto A. Ibarra-Ramírez

摘要

This study presents an innovative project for the quantification and morphological characterization of PC12 cell cultures, offering a comparative analysis of two methods: ImageJ software and a newly developed Python algorithm. Utilizing images captured at specific intervals on days 1, 3, and 5, both methods involved preprocessing steps such as brightness and contrast adjustment, segmentation, and morphological operations, followed by particle analysis for quantification. The results showed no significant differences between the methods in measuring the area occupied by the cells and cluster sizes on the same day. However, both methods revealed statistically significant increases in cell proliferation between days. The Python algorithm, built with libraries such as OpenCV and Numpy, offers additional advantages, including automation, a graphical user interface (GUI) created in Visual Studio Code and easy data export to Excel. These features make it a feasible and effective tool for real-time cell analysis, complementing and potentially enhancing traditional methods. The study concludes that the proposed system is as accurate and efficient as ImageJ, with added operational flexibility, providing a robust alternative for quantitative cell culture analysis. This advancement holds significant promise for applications in tissue engineering and regenerative medicine.