Search

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

Pinhole‐shaped light‐emitting diode (LED) arrays with dimension ranging from 100 μm down to 5 μm have been developed as point illumination sources. The proposed microLED arrays, which are based on gallium nitride (GaN) technology and emitting in the blue spectral region (λ = 465 nm), are integrated into a compact lensless holographic microscope for a non‐invasive, label‐free cell sensing and imaging. From the experimental results using single pinhole LEDs having a diameter of 90 μm, the reconstructed images display better resolution and enhanced image quality compared to those captured using a commercial surface‐mount device (SMD)‐based LED.