Speaker
Description
Optical microscopy is a powerful and minimally invasive tool for the investigation of biological processes. In this context, processing of images is of utmost importance to improve image quality and sample understanding. However, there is not a standard quantitative approach to evaluate image quality, especially in presence of artifacts. The computation of metrics can provide ambiguous results, with poor agreement of the metrics with human visual perception. In addition, the ground truth is often needed for comparison.
To address these issues, we performed a systematic study to identify markers and metrics for the characterization and evaluation of microscopic images. We developed simple models for simulation of biological structures and the most common microscopic artifacts; these include blurring, mixed Poisson-Gaussian noise, and uneven illumination. The models can be applied by tuning independent parameters to modulate the sample structure or the specific effect of the artifact. The metrics for image evaluation were selected after extensive literature research, taking as reference previous studies on microscopic measurements. We obtained a collection of images with a variety of simulated experimental conditions and specific trends of the metrics were identified for each artifact, developing an overview of reference markers for different degradations. Finally, image markers were validated on real experimental datasets. These results help the understanding of experimental acquisitions and should be considered when evaluating the effect of different processing workflows on the same input image.
