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Virtual microscopes are devices that employ an automated XYZ mechanism to scan a sample, leading to the obtention of a series of small pictures that, when merged, compose a high-quality representation of the specimen. Due to the assembly tolerances, these devices may suffer from zones out of focus, reducing the quality of the final image. To solve this problem, researchers employ evaluation methods to calculate the blurriness of the image, and when an out of focus picture is located, performs the process of autofocus. Because of the variation on the types of samples, especially in pathology, the existing evaluation methods may fail to deliver a proper blur detection. This article proposes an optimized algorithm for the detection of the blurriness while conducting the sample scan in real time, ensuring that every scanned picture will be in focus. For this purpose, the algorithm relies on two functions, the comparison of the overlapping zones of two consecutive images, and the multivariate linear regression of a series of focus functions. The algorithm proved to be a reliable tool when applied in different pathology samples.

An Overlapping and Integral Blurry Evaluation Method to Optimize Tissues Scanning from Microscope