Parallel implementations to accelerate the autofocus process in microscopy applications

Juan C Valdiviezo-N; Francisco J Hernandez-Lopez; Carina Toxqui-Quitl

doi:10.1117/1.JMI.7.1.014001

Parallel implementations to accelerate the autofocus process in microscopy applications

J Med Imaging (Bellingham). 2020 Jan;7(1):014001. doi: 10.1117/1.JMI.7.1.014001. Epub 2020 Jan 17.

Authors

Juan C Valdiviezo-N¹, Francisco J Hernandez-Lopez², Carina Toxqui-Quitl³

Affiliations

¹ CONACYT-Centro de Investigación en Ciencias de Información Geoespacial, Yucatán, México.
² CONACYT-Centro de Investigación en Matemáticas A. C., Unidad Mérida, México.
³ Universidad Politécnica de Tulancingo, Computer Vision Laboratory, Hidalgo, México.

Abstract

Several autofocus algorithms based on the analysis of image sharpness have been proposed for microscopy applications. Since autofocus functions (AFs) are computed from several images captured at different lens positions, these algorithms are considered computationally intensive. With the aim of presenting the capabilities of dedicated hardware to speed-up the autofocus process, we discuss the implementation of four AFs using, respectively, a multicore central processing unit (CPU) architecture and a graphic processing unit (GPU) card. Throughout different experiments performed on 300 image stacks previously identified with tuberculosis bacilli, the proposed implementations have allowed for the acceleration of the computation time for some AFs up to 23 times with respect to the serial version. These results show that the optimal use of multicore CPU and GPUs can be used effectively for autofocus in real-time microscopy applications.

Keywords: Hyper-Q; autofocus functions; graphic processing unit; microscopy images; multicore central processing unit; nested parallelism; parallel computing; tuberculosis dataset.