5 research outputs found
Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope
Cellphones equipped with high-quality cameras and powerful CPUs as well as
GPUs are widespread. This opens new prospects to use such existing
computational and imaging resources to perform medical diagnosis in developing
countries at a very low cost.
Many relevant samples, like biological cells or waterborn parasites, are
almost fully transparent. As they do not exhibit absorption, but alter the
light's phase only, they are almost invisible in brightfield microscopy.
Expensive equipment and procedures for microscopic contrasting or sample
staining often are not available.
By applying machine-learning techniques, such as a convolutional neural
network (CNN), it is possible to learn a relationship between samples to be
examined and its optimal light source shapes, in order to increase e.g. phase
contrast, from a given dataset to enable real-time applications. For the
experimental setup, we developed a 3D-printed smartphone microscope for less
than 100 \$ using off-the-shelf components only such as a low-cost video
projector. The fully automated system assures true Koehler illumination with an
LCD as the condenser aperture and a reversed smartphone lens as the microscope
objective. We show that the effect of a varied light source shape, using the
pre-trained CNN, does not only improve the phase contrast, but also the
impression of an improvement in optical resolution without adding any special
optics, as demonstrated by measurements
Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope
Cellphones equipped with high-quality cameras and powerful CPUs as well as
GPUs are widespread. This opens new prospects to use such existing
computational and imaging resources to perform medical diagnosis in developing
countries at a very low cost.
Many relevant samples, like biological cells or waterborn parasites, are
almost fully transparent. As they do not exhibit absorption, but alter the
light's phase only, they are almost invisible in brightfield microscopy.
Expensive equipment and procedures for microscopic contrasting or sample
staining often are not available.
By applying machine-learning techniques, such as a convolutional neural
network (CNN), it is possible to learn a relationship between samples to be
examined and its optimal light source shapes, in order to increase e.g. phase
contrast, from a given dataset to enable real-time applications. For the
experimental setup, we developed a 3D-printed smartphone microscope for less
than 100 \$ using off-the-shelf components only such as a low-cost video
projector. The fully automated system assures true Koehler illumination with an
LCD as the condenser aperture and a reversed smartphone lens as the microscope
objective. We show that the effect of a varied light source shape, using the
pre-trained CNN, does not only improve the phase contrast, but also the
impression of an improvement in optical resolution without adding any special
optics, as demonstrated by measurements
A Simply Equipped Fourier Ptychography Platform Based on an Industrial Camera and Telecentric Objective
Fourier ptychography microscopy (FPM) is a recently emerged computational imaging method, which combines the advantages of synthetic aperture and phase retrieval to achieve super-resolution microscopic imaging. FPM can bypass the diffraction limit of the numerical aperture (NA) system and achieve complex images with wide field of view and high resolution (HR) on the basis of the existing microscopic platform, which has low resolution and wide field of view. Conventional FPM platforms are constructed based on basic microscopic platform and a scientific complementary metal–oxide–semiconductor (sCMOS) camera, which has ultrahigh dynamic range. However, sCMOS, or even the microscopic platform, is too expensive to afford for some researchers. Furthermore, the fixed microscopic platform limits the space for function expansion and system modification. In this work, we present a simply equipped FPM platform based on an industrial camera and telecentric objective, which is much cheaper than sCMOS camera and microscopic platform and has accurate optical calibration. A corresponding algorithm was embedded into a conventional FP framework to overcome the low dynamic range of industrial cameras. Simulation and experimental results showed the feasibility and good performance of the designed FPM platform and algorithms
New quantitative phase imaging modalities on standard microscope platforms
Three new reconstruction methods for quantitative phase imaging, including two interrelated two-dimensional methods, called multifilter phase imaging with partially coherent light and phase optical transfer function recovery, which lead to a third three-dimensional method, called tomographic deconvolution phase microscopy, were developed in response to a growing need among biomedical end users for solutions which can be integrated on standard microscope platforms. The performance of these new methods were evaluated using modelling and simulation as well as experimentation with known test cases. In addition to the development of new methods, existing methods for quantitative phase imaging were applied to characterize the effects of manufacturing, cleaving, and fusion splicing in large-mode-area erbium- and ytterbium-doped optical fibers.Ph.D