Single-exposure, on-axis Gabor holographic microscopy has been shown to
work reasonably well for 3-D cell identification, with a large gain in simplicity,
compactness and expense.
replaces the image forming role of the microscope objective. Although not applicable to quantitative phase measurement, single-exposure, on-axis Gabor holographic microscopy has been shown to work reasonably well for 3-D cell identification, with a large gain in simplicity, compactness and expense that is very valuable for point-of-care health solutions. With either on-axis or off-axis configurations, the digital holograms are recorded by a CCD sensor and transferred to a computer for processing. One can then reconstruct the specimen’s 3-D image, or a stack of 2-D images, from the recorded digital hologram by inverse Fresnel transformation. For the automated identification of biological specimens, signal processing and/or statistical pattern recognition algorithms can be used to extract the unique features of the cell under inspec- tion from its reconstructed complex-valued images. For instance, a segmentation algorithm may be used to
separate the cells from the background in the reconstructed
image. After 3-D segmentation, sampling feature vectors can be
generated by randomly extracting the complex pixel values in
the segmented 3-D image. Alternatively, one can directly apply
pattern recognition analysis to the digital hologram data to identify the cells. This approach would remove the 3-D holographic
reconstruction step, making the computations faster.
Phil Saunders/ spacechannel.org
A number of statistical classification algorithms are developed particularly for the identification of biological specimens.
Cross-comparison with 2-D intensity images has shown that
complex 3-D images that are reconstructed from digital holograms provide more discriminating features and allow for
better classification of cells.
One way to simplify the system is to implement a Gabor-mode DHM with a partially coherent source. In this configuration, ballistic photons automatically provide the reference
beam. The photons pass through the specimen and its surrounding medium without any scattering. The use of partially
coherent sources such as LEDs (as opposed to laser beams) provides a lower cost and more compact 3-D imaging instrument.
To improve identification and recognition, one may apply
wavelet transformation to the recorded digital hologram prior
to reconstruction in order to extract the discriminating features of the cells under inspection. Unlike the Fourier transform family, which solely provides frequency analysis, wavelets
provide space-frequency analysis that is beneficial in the study
of complex objects involving local features.
Another interesting application of DHM is in the analysis
of embryonic stem cells, which are being explored for their
potential as a therapeutic solution in regenerative medicine. It
is important for biologists to conduct quantitative monitoring
in order to better understand stem cells’ characteristics over
[ Gabor digital holographic microscopy ]
(a) DHM with Gabor configuration under partially coherent illumination for cell identification. (b) Digital hologram of a sunflower
stem cell reconstructed after applying Gabor wavelet filtering.
(c, d) Statistical sampling distributions show improved separation between nontraining true and false class samples when
(d) Gabor-filtered digital holograms are used instead of (c) nonfiltered holograms. Null hypothesis is the training true class.
Computed phase distribution of a three-day-old embryonic stem
cell colony using DHM. Microscope objective is 20X with 0.4 NA.
(Left) Computed phase distribution and (center) 3-D optical path
length distribution. (Right) Cell thickness profile along x (solid
line) and y (dotted line) directions.
their development cycle in the colony. Combined with statistical analysis tools, DHM is a useful inspection modality for
stem cell applications due to its noninvasiveness and recognition capabilities. This combination can be used for monitoring
the progression of stem cell colonies and quantifying their
evolution and differentiation over time.
The figure above shows the computed phase distribution
of a three-day-old stem cell colony. Time-lapsed holographic