5,451 research outputs found
Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region
A dual-wavelength thulium-doped fluoride
fiber (TDFF) laser is presented. The generation of the TDFF
laser is achieved with the incorporation of a single modemultimode-
single mode (SMS) interferometer in the laser
cavity. The simple SMS interferometer is fabricated using the
combination of two-mode step index fiber and single-mode fiber.
With this proposed design, as many as eight stable laser lines
are experimentally demonstrated. Moreover, when a tunable
bandpass filter is inserted in the laser cavity, a dual-wavelength
TDFF laser can be achieved in a 1.5-μm region. By heterodyning
the dual-wavelength laser, simulation results suggest that the
generated microwave signals can be tuned from 105.678 to
106.524 GHz with a constant step of �0.14 GHz. The presented
photonics-based microwave generation method could provide
alternative solution for 5G signal sources in 100-GHz region
Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices
This paper proposes an efficient probabilistic method that computes
combinatorial gradient fields for two dimensional image data. In contrast to
existing algorithms, this approach yields a geometric Morse-Smale complex that
converges almost surely to its continuous counterpart when the image resolution
is increased. This approach is motivated using basic ideas from probability
theory and builds upon an algorithm from discrete Morse theory with a strong
mathematical foundation. While a formal proof is only hinted at, we do provide
a thorough numerical evaluation of our method and compare it to established
algorithms.Comment: 17 pages, 7 figure
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