An automated method for removal of striping artifacts in fluorescent whole-slide microscopy

Whole slide scanners often acquire images of tissue sections that are larger than their field of view through tile or line-scanning. The subsequently stitched or aligned images can often suffer from imaging artifacts such as horizontal or vertical stripes. These stripes degrade the image quality in fluorescent biological imaging samples and can also limit the accuracy of any subsequent analyses such as cell segmentation.

We propose a novel data-driven method of removing stripe artifacts in stitched biological images based on the location of the stripes, background modeling, and illumination correction. This method provides an automated way of removing the stripes of an individual image while preserving image details and quality for subsequent analyses.

The results were assessed using both qualitative and quantitative metrics and the algorithm has proven very effective in removing the stripe artifacts from hundreds of brain images.

Several metrics were used to quantify the effectiveness of our proposed method compared to other published techniques. Images with simulated artifacts were created so that full-reference metrics could be applied to demonstrate the applicability of the algorithm for a wider variety of illumination profiles.

We describe a data analysis pipeline that allows for automatic removal of stripes caused by line-scanning. Our proposed method can be applied without the need for separate blank field of view images or use of image batches to model the background, so it is suitable for real-time parallel batch processing of large datasets.