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Automated label-free quantitative imaging of biological samples can greatlybenefit high throughput diseases diagnosis. Digital holographic microscopy(DHM) is a powerful quantitative label-free imaging tool that retrievesstructural details of cellular samples non-invasively. In off-axis DHM, aproper spatial filtering window in Fourier space is crucial to the quality ofreconstructed phase image. Here we describe a region-recognition approach thatcombines shape recognition with an iterative thresholding to extracts theoptimal shape of frequency components. The region recognition technique offersfully automated adaptive filtering that can operate with a variety of samplesand imaging conditions. When imaging through optically scattering biologicalhydrogel matrix, the technique surpasses previous histogram thresholdingtechniques without requiring any manual intervention. Finally, we automate theextraction of the statistical difference of optical height between malariaparasite infected and uninfected red blood cells. The method described herepave way to greater autonomy in automated DHM imaging for imaging live cell inthick cell cultures.

Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy