65 research outputs found
Total variation denoising in anisotropy
We aim at constructing solutions to the minimizing problem for the variant of
Rudin-Osher-Fatemi denoising model with rectilinear anisotropy and to the
gradient flow of its underlying anisotropic total variation functional. We
consider a naturally defined class of functions piecewise constant on
rectangles (PCR). This class forms a strictly dense subset of the space of
functions of bounded variation with an anisotropic norm. The main result shows
that if the given noisy image is a PCR function, then solutions to both
considered problems also have this property. For PCR data the problem of
finding the solution is reduced to a finite algorithm. We discuss some
implications of this result, for instance we use it to prove that continuity is
preserved by both considered problems.Comment: 34 pages, 9 figure
Atomap: a new software tool for the automated analysis of atomic resolution images using two-dimensional Gaussian fitting
Non-local Means for Scanning Transmission Electron Microscopy Images and Poisson Noise based on Adaptive Periodic Similarity Search and Patch Regularization
Polar space based shape averaging for star-shaped biological objects
In this paper, we propose an averaging method for expert segmentation proposals of microbial organisms, resulting in a smooth, naturally looking segmentation ground truth. The approach exploits a geometrical property of the majority of the organisms - star-shapedness - and is based on contour averaging in polar space. It is robust and computationally efficient, where robustness is due to the absence of tuneable parameters. Moreover, the algorithm preserves the uncertainty (in terms of the standard deviation) of the experts' opinion, which allows to introduce an uncertainty-aware metric for estimation of the segmentation quality. This metric emphasizes the influence of ground truth regions with low variance. We study the performance of the proposed averaging method on time-lapse microscopy data of Corynebacterium glutamicum and the uncertainty-aware metric on synthetic data
3D-MRT und 2D-Photo-Registrierung mit der JIRDED (Joint Image Registration, Denoising and Edge Detection) Methode
A Hybrid Approach for Automated Characterization of CatIBs Production in a Biotechnological Screening System
Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems
We here propose an automated pipeline for the microscopy image-based
characterization of catalytically active inclusion bodies (CatIBs), which
includes a fully automatic experimental high-throughput workflow combined with
a hybrid approach for multi-object microbial cell segmentation. For automated
microscopy, a CatIB producer strain was cultivated in a microbioreactor from
which samples were injected into a flow chamber. The flow chamber was fixed
under a microscope and an integrated camera took a series of images per sample.
To explore heterogeneity of CatIB development during the cultivation and track
the size and quantity of CatIBs over time, a hybrid image processing pipeline
approach was developed, which combines an ML-based detection of in-focus cells
with model-based segmentation. The experimental setup in combination with an
automated image analysis unlocks high-throughput screening of CatIB production,
saving time and resources.
Biotechnological relevance - CatIBs have wide application in synthetic
chemistry and biocatalysis, but also could have future biomedical applications
such as therapeutics. The proposed hybrid automatic image processing pipeline
can be adjusted to treat comparable biological microorganisms, where fully
data-driven ML-based segmentation approaches are not feasible due to the lack
of training data. Our work is the first step towards image-based bioprocess
control
High-precision scanning transmission electron microscopy at coarse pixel sampling for reduced electron dose
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