PhD Proposal (oct. 2015 – sept. 2018)
GEOMETRICAL MODELING AND CHARACTERIZATION OF DENSE PARTICLE POPULATIONS USING IMAGE ANALYSIS AND STOCHASTIC GEOMETRY:
APPLICATION TO EMULSIONS, SUSPENSIONS AND FLOWS IN NUCLEAR REPROCESSING PROCESSES.
Supervisor: Johan Debayle (UMR CNRS 5307 LGF – MINES Saint-Étienne) Cosupervisors: Fabrice Lamadie (DTEC/SGCS/LGCI-CEA MARCOULE)
Sophie Charton (DTEC/SGCS/LGCI-CEA MARCOULE)
Location: CEA Marcoule, FRANCE
Context
Most of the steps of the nuclear fuel reprocessing process involve multiphasic systems / flows, like liquid-liquid extraction, leaching, filtration, precipitation, etc. For all of these applications the knowledge of the main properties of the dispersed phase (hold-up, particle shape, particle size distribution, etc.) is a key issue. Today, interferometric techniques (holography, global rainbow refractometry) as well as images acquisition coupled to image analysis are used to estimate the characteristics of these particles (drops, bubbles, and crystals). Nevertheless, these quantitative measurements are generally based on restrictive assumptions (low density of particles, isotropic particles, etc.) which represent a strong limitation for the scope of investigation.
Research works for analyzing populations of particles with a high density have shown the limitations of low-level image processing methods based on particles individualization and characterization. To overcome these limitations, approaches based on stochastic geometry are a promising alternative.
The main objective of this PhD thesis is the development of image analysis methods for characterizing the geometry and morphology of a dense population of particles by using stochastic geometrical tools, adapted to the encountered configurations (bubbles, droplets, crystals ...).
(a) liquid-gaz flow (b) emulsion
State of the art
Regarding the literature, the CEA laboratory has already developed three main methods for analyzing such images which are based on mathematical morphology [1], Hough transform [2] and pattern matching [3]. Such methods revealed good performances for the detection of droplets under the usual conditions prevailing in extraction processes. However, they reach their limits and are not appropriate to the most demanding configurations encountered in R&D studies (viscous liquids, bubbles, anisotropic solids), where the inclusion of higher density particles (involving many overlapping with the 2-D projections) as well as specific morphologies (e.g. elongated objects) require specific developments.
The research works carried out at the Ecole des Mines de Saint-Etienne [4, 5, 6, 7] for characterizing dense populations of anisotropic solids, have highlighted the limitations of such methods. Indeed, as they are mainly based on particles individualization or matching, this quickly becomes impossible as particles overlap.
A promising alternative concerns stochastic geometrical methods [8, 9, 10]. The purpose is to geometrically model the population of particles with random sets so as to simulate synthetic images representative of the real data. They address some limitations related to the population density by providing, without any step of object individualization, the geometrical characteristics of individual particles (average area, average perimeter, number) [8, 11, 12]. However, when the particles are anisotropic, there is much less theoretical results to fit the model to the data and therefore to estimate their morphological characteristics (elongation, sphericity ...) [13, 14].
Objective and working plan
The aim of the thesis is to develop and study stochastic geometrical methods for characterizing the geometry and the morphology of dense particle populations such as concentrated emulsions, flows of non-spherical bubbles and solid particles encountered in chemical engineering processes.
After a literature review, stochastic geometry tools will be proposed and implemented to characterize images of dense particle populations from actual cases. For this purpose synthetic images will be simulated using a stochastic model for which both its geometric and morphological parameters (density, size distribution, shape, orientation ...) should be adjusted by statistical comparison to the real images, via analytical expression or numerical optimization. For each configuration (bubbles, crystals, etc.), the methodology will be based on the following steps:
Development of a stochastic model (Boolean model, dead leaves, random field, etc.) and simulation of synthetic images,
Characterization of synthetic and real images, i.e. extraction of descriptors (total area, covariance, Euler number, etc.),
Identification of the model parameters by comparing these descriptors.
The result of the parameters identification will provide 2D geometrical (area), topological (number of objects) and morphological (shape factor) characteristics of the individual particles in a statistical sense.
It can be extended to obtain 3D information using stereological approaches.
Finally, an important part of the study will consist in proposing, for each configuration, a validation system based on numerical and/or experimental studies, so as to evaluate the performance of the proposed methods. For this purpose, the important database already available in both laboratories can be used.
Bibliography
[1] A. Khalil, F. Puel, Y. Chevalier, J. M. Galven, A. Rivoire, J. P. Klein, Chem. Eng. J. 2012, 165, 946.
[2] Hans-Jörg Bart, Matthias Mickler, and Hanin B. Jildeh. Optical Image Analysis and Determination of Dispersed Multi Phase Flow for Simulation and Control in Optical Imaging: Technology, Methods and Applications. Nova Science Publishers, 2012.
[3] S. Maaß, J. Rojahn, R. Hänsch and M. Kraume. Automated drop detection using image analysis for online particle size monitoring in multiphase systems. Computers & Chemical Engineering, 45: 27 – 37, 2012.
[4] O. Ahmad, J. Debayle, N. Gherras, B. Presles, G. Fevotte, and J. C. Pinoli. Quantification of overlapping polygonal-shaped particles based on a new segmentation method of in situ images during crystallization. Journal of Electronic Imaging, 21(2):1-12, 2012.
[5] B. Presles, J. Debayle, and J. C. Pinoli. Size and shape estimation of 3-D convex objects from their 2-D projections. Application to crystallization processes. Journal of Microscopy, 248(2):140-155, 2012.
[6] O. Ahmad, J. Debayle, and J. C. Pinoli. A geometric-based method for recognizing overlapping polygonal-shaped and semi-transparent particles in gray tone images. Pattern Recognition Letters, 32(15):2068-2079, 2011.
[7] B. Presles, J. Debayle, G. Fevotte and J. C. Pinoli. A novel image analysis method for in-situ monitoring the particle size distribution of batch crystallization processes. Journal of Electronic Imaging, 19(3):1-7, 2010.
[8] S.N. Chiu, D. Stoyan, W.S. Kendall and J. Mecke. Stochastic geometry and its applications. Wiley, 2013.
[9] I. S. Molchanov. Statistics of the Boolean Model for Practitioners and Mathematicians. Wiley, Chichester 1997.
[10] K. Michielsen and H. De Raedt. Integral geometry morphological image analysis. Physical Reports, 347:461-538, 2001.
[11] R. Schneider and W. Weil. Stochastic and Integral Geometry. Springer, Berlin Heidelberg, 2008.
[12] M. Berchtold. Modelling of Random Porous Media using Minkowski Functionals. PhD Thesis, ETH Zurich, Swiss, 2008.
[13] S. Rivollier, J. Debayle and J. C. Pinoli. Shape diagrams for 2D compact sets - Part I: analytic convex sets. Australian Journal of Mathematical Analysis and Applications, 7(2-3):1-27, 2010.
[14] S. Rivollier, J. Debayle and J. C. Pinoli. Adaptive shape diagrams for multiscale morphometrical image analysis. Journal of Mathematical Imaging and Vision, 49(1):51-68, 2014.
Candidate profile
Candidates should have a Master of Science in applied mathematics and/or image analysis
Programming skills with Matlab and C/C++
A good level of written and spoken English
Contact
Candidates should send cover letter, CV and reference letters to Johan DEBAYLE (debayle@emse.fr), Sophie CHARTON (sophie.charton@cea.fr) and Fabrice LAMADIE (fabrice.lamadie@cea.fr).
