Abstract
Particle estimation is a classical problem arising in many science fields, such as biophysics, fluid mechanics and biomedical imaging. Many interesting applications in these areas involve 3D imaging data: This work presents a technique to estimate the 3D coordinates of the center of spherical particles. This procedure has its core in the processing of the images of the scanned volume: It firstly applies denoising techniques to each frame of the scanned volume and then provides an estimation of both the center and the profile of the 2D intersections of the particles with the frames, by coupling the usage of Total Variation functional and of a regularized weighted Least Squares fit. Then, the 2D information is used to retrieve the 3D coordinates using geometrical properties. The experiments provide evidence that image denoising has a large impact on the performance of the particle tracking procedures, since they strongly depend on the quality of the initial acquisition. This work shows that the choice of tailored image denoising technique for Poisson noise leads to a better estimation of the particle positions.
Highlights
Particle tracking techniques are widely employed in several science fields for identifying particular structures or Funding for this project was provided in part by LABEX MMCD and ANR CoMeDIC.Alessandro Benfenati: Member of the INdAM Research Group GNCS.It has been pointed out [21] that particles have different meanings depending on the applications: a single molecule, a virus, a spherical object
The very low error on the radius estimation suggests that this procedure improves a priori information about the radius of particles of uncertain dimension
This work demonstrates that the preprocessing of the frames requires tailored techniques, depending on noise type: Since Poisson noise is the most common noise affecting the images, simple Gaussian filtering is not sufficient
Summary
Alessandro Benfenati: Member of the INdAM Research Group GNCS It has been pointed out [21] that particles have different meanings depending on the applications: a single molecule, a virus, a spherical object. Several procedures have been proposed to estimate the particle position: cross-correlation of a sequence of images [35], centroid techniques [33] and Gaussian fitting [38]. The first step of particle tracking problem is solved: The proposed algorithm provides estimations of the particles position with subpixel resolution, both in two- and three-dimensional cases. The analysis focuses on the role of image denoising techniques, which heavily influences the final result and performance of position estimation algorithms. I denotes the identity matrix and 0 the vector with all zeros entries
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