The differential interference contrast (DIC) microscope is commonly
used for the visualization of live biological specimens. It enables
the view of the transparent specimens while preserving their
viability, being a non-invasive modality. Fertility clinics often
use the DIC microscope for evaluation of human embryos quality.
Towards quantification and reconstruction of the visualized
specimens, an image formation model for DIC imaging is sought and
the interaction of light waves with biological matter is examined.
In many image formation models the light-matter interaction is
expressed via the first Born approximation. The validity region of
this approximation is defined in a theoretical bound which limits
its use to very small specimens with low dielectric contrast. In
this work the Born approximation is investigated via the Helmholtz
equation, which describes the interaction between the specimen and
light. A solution on the lens field is derived using the Gaussian
Legendre quadrature formulation. This numerical scheme is considered
both accurate and efficient and has shortened significantly the
computation time as compared to integration methods that required a
great amount of sampling for satisfying the Whittaker - Shannon
sampling theorem. By comparing the numerical results with the
theoretical values it is shown that the theoretical bound is not
directly relevant to microscopic imaging and is far too limiting.
The numerical exhaustive experiments show that the Born
approximation is inappropriate for modeling the visualization of
thick human embryos.
The objective of the current study is to develop an automatic tool to identify bacterial types using computer-vision and statistical modeling techniques. Bacteriophage (phage)-typing methods are used to identify and extract representative profiles of bacterial types, such as the Staphylococcus Aureus. Current systems rely on the subjective reading of plaque profiles by human expert. This process is time-consuming and prone to errors, especially as technology is enabling the increase in the number of phages used for typing. The statistical methodology presented in this work, provides for an automated, objective and robust analysis of visual data, along with the ability to cope with increasing data volumes.