Development of a computational tool based on image processing for the quality control automation of lead based personal protection equipment
Keywords:
PPE, quality control, crack, image processing, rejection criteria
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Copyright (c) 2024 John Camilo Alcalde Poveda, Erika Muñoz Arango
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Purpose: The use of leaded personal protective equipment (PPE) is a radiological protection measure against scattered radiation generated in medical procedures involving the use of equipment or sources that emit ionizing radiation. Periodic quality controls (QC) with tomography images make it possible to ensure the condition and absence of cracks in PPE for routine clinical use. By means of acceptance or rejection criteria it is possible to decide whether it is viable and safe to continue using them in order to guarantee adequate radiological protection to the individual. The objective of this work is to automate the QC of PPE by developing a computational tool using an image processing approach and applying acceptance and rejection criteria based on the quantification of the cracks.
Methods: Digital images of the PPE’s were acquired by computed tomography (CT) as a basis for the creation and parameterization of an optimization tool using Python language. An algorithm was developed that changed from DICOM format to grayscale PNG to optimize the analysis and processing of the image information. Image processing techniques and methods were applied on these PNG images that allow the tool to classify PPE according to their type, plumbed vests and thyroid protectors. Subsequently, a model was programmed to detect possible cracks in the PPE and a configuration was created to allow manual selection of objects that are not to be quantified. Finally, the tool was programmed to calculate the total area of cracks detected in each PPE by applying correction factors and rejection or acceptance criteria according to the total area of cracks identified in the QC.
Results: The tool satisfactorily classified all the PPE that were evaluated, adequately classifying the 30 vests and 21 lead collars. Of the above, the program detected 9 PPE with possible cracks, all of them being vests, of which 5 actually had cracks in the lead and the other 4 only showed pronounced folds. The total area of cracks ranged from 6.1 to 12.5 . Finally, the tool yielded rejection criteria for only one of the 5 vests showing cracks, considering that the allowable limit area is up to 10 according to the literature.
Conclusions: The development of the proposed tool using image processing techniques for the detection and quantification of fissures is viable as a method of QC automation,achieving a good identification of the fissures with an accuracy greater than 97%. The use of computational tools for the optimization of radiation protection processes allows reducing time and observer dependence in the execution of QC.
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