We developed an automated high-resolution full-field spatial coherence tomography (FF-SCT) microscope for quantitative phase imaging that is based on the spatial, rather than the temporal, coherence gating. The Red and Green color laser light was used for finding the quantitative phase images of unstained human red blood cells (RBCs). This study uses morphological parameters of unstained RBCs phase images to distinguish between normal and infected cells. We recorded the single interferogram by a FF-SCT microscope for red and green color wavelength and average the two phase images to further reduced the noise artifacts. In order to characterize anemia infected from normal cells different morphological features were extracted and these features were used to train machine learning ensemble model to classify RBCs with high accuracy.
To quantitatively obtain the phase map of Onion and human red blood cell (RBC) from white light interferogram we used Hilbert transform color fringe analysis technique. The three Red, Blue and Green color components are decomposed from single white light interferogram and Refractive index profile for Red, Blue and Green colour were computed in a completely non-invasive manner for Onion and human RBC. The present technique might be useful for non-invasive determination of the refractive index variation within cells and tissues and morphological features of sample with ease of operation and low cost.