We introduce an inverse method for determining simultaneously the real and imaginary refractive indices of microspheres based on integrating sphere measurements of diffuse reflectance and transmittance, and Monte Carlo modelling in conjunction with the Mie theory. The results for polystyrene microspheres suspended in water are presented.

We constructed an automated reflectometry system for accurate measurement of coherent reflectance curves of turbid samples and analyzed the presence of coherent and diffuse reflection near the specular reflection angle. An existing method has been validated to determine the complex refractive indices of turbid samples on the basis of nonlinear regression of the coherent reflectance curves by Fresnel's equations. The complex refractive indices of fresh porcine skin epidermis and dermis tissues and Intralipid solutions were determined at eight wavelengths: 325, 442, 532, 633, 850, 1064, 1310, and 1557 nm.

Diffraction imaging of polystyrene spheres and B16F10 mouse melanoma cells embedded in gel has been investigated with a microscope objective. The diffraction images acquired with the objective from a sphere have been shown to be comparable to the Mie theory based projection images of the scattered light if the objective is translated to defocused positions towards the sphere. Using a confocal imaging based method to reconstruct and analyze the 3D structure, we demonstrated that genetic modifications in these cells can induce morphological changes and the modified cells can be used as an experimental model for study of the correlation between 3D morphology features and diffraction image data. Diffraction images of spheres (left column) and melanoma cells (right column) acquired with an objective.

Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells.

Diffraction images record angle-resolved distribution of scattered light from a particle excited by coherent light and can correlate highly with the 3D morphology of a particle. We present a jet-in-fluid design of flow chamber for acquisition of clear diffraction images in a laminar flow. Diffraction images of polystyrene spheres of different diameters were acquired and found to correlate highly with the calculated ones based on the Mie theory. Fast Fourier transform analysis indicated that the measured images can be used to extract sphere diameter values. These results demonstrate the significant potentials of high-throughput diffraction imaging flow cytometry for extracting 3D morphological features of cells.