Optical microscopy is widely applied to the investigation of biomedical samples, and a variety of image processing approaches have been established to reduce artifacts generated by the measurement process. However, a standardized and reliable method for assessing image quality is still lacking. Our study contributes to the investigation of image evaluation methods for fluorescence microscopy. We present a set of no-reference metrics that can be used for the characterization of experimental artifacts. In addition, our method is incorporated into a machine learning approach for automatic classification of single artifacts. The metrics identify reliable markers for single artifacts in fluorescence microscopy measurements, can be easily interpreted, and allow the selection of the best image based on specific quality requirements. Our study provides a simple evaluation tool for optical microscopy that can also be extended to the different stages of the processing pipeline.
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