A Lensless Multifocal Fusion Imaging Method Focused on Microfluidic Chips for IoMT-Based Cell Analysis System

The combination of lensless diffraction imaging and microfluidics provides a relevant solution for the lab-on-a-chip analysis of cells in the context of the Internet of Medical Things (IoMT). However, the cells are on multiple planes in microfluidics, and they only can be sensed by means of complex algorithms. Taking into account the limited resources available on edge computing, a lightweight multifocal fusion reconstruction algorithm is proposed in this work, which still keeps a high-quality reconstruction. The reconstruction, autofocus, and multifocal fusion are integrated, and the reference light normalization and the object light extraction are achieved to adapt their combination. The integrated process is divided into two stages: 1) the cell diffraction image segmentation and 2) the multifocal fusion, based on mono-focal object light reconstruction and masking, respectively. The single autofocus with the golden section search algorithm is merged into the two stages. Finally, a validation system is built based on an FPGA, with a size of only 45 mm $\times 45$ mm $\times 75$ mm. The average error for $8~\mu $ m diameter microspheres is 4.67%. For the white blood cells, the evaluation functions of the criteria, the energy of gradient (EOG), and the variance are improved by 12.2%, 92.3%, and 57.3%, respectively. Under the same accuracy, the global number of searches for focus is reduced by 47%–59%, compared to the two-step golden section method, whereas the global speed is also improved by 53%.