Fitting gaussian curves on particle images for density estimation

Abstract

We use a 3D printed device to measure the density of a micro-particle with acoustofluidics, which consists in using sound waves to trap particles in free space. Initially, the particle is trapped in the microscope’s focal plane (no blur). Then the transducers are shut off and the particle falls inside the fluid, increasing its diameter due to defocus caused by the distance to the lens. This increase in diameter along time provides its velocity, which can, in turn, be used to compute its density. To manually annotate the diameter in the recorded images is a tedious task and is prone to errors. That happens due to the high noise present in the images, specially in the last frames where the defocus is high. Because of that, we use a 2D Gaussian model fitting process to estimate the particle diameter throughout different depth frames. To find the diameters, we initially perform the Gaussian parameters fit with Genetic Algorithm in each frame of the recorded particle trajectory to avoid local minima. Then we refine the fit with Gradient Descent using Tensorflow in order to compensate for any randomness present in the fit of the Genetic Algorithm. We validate the method by retrieving a known particle’s density with acceptable performance.