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Computational analysis of wide-angle light scattering from single cells Open Access


Other title
image analysis
electrical engineering
inverse analysis
pattern recognition
structure prediction
3D structure prediction
medical diagnostics
reverse monte carlo
cell morphology
biomedical image analysis
shape recognition
scattering theory
optical simulation
computer engineering
computer science
machine learning
computational analysis
image processing
iterative methods
inverse scattering problem
light scattering
wide-angle light scattering
computational intelligence
image parameterization
fourier theory
lab on a chip
cellular optics
scattering simulation
generate and test
fourier transform
single cells
pattern analysis
Type of item
Degree grantor
University of Alberta
Author or creator
Pilarski, Patrick Michael
Supervisor and department
Backhouse, Christopher J. (Electrical and Computer Engineering)
Examining committee member and department
Wheeler, Arron (Chemisty, University of Toronto)
Bischof, Walter F. (Computing Science)
Reformat, Marek (Electrical and Computer Engineering)
Musilek, Petr (Electrical and Computer Engineering)
Cockburn, Bruce (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use. This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.
License granted by Patrick Pilarski ( on 2009-10-01T16:48:45Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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