scholarly journals Quantifying soot nanostructures: Importance of image processing parameters for lattice fringe analysis

2020 ◽  
Vol 211 ◽  
pp. 430-444 ◽  
Author(s):  
Sebastian A. Pfau ◽  
Antonino La Rocca ◽  
Michael W. Fay
2016 ◽  
Vol 76 ◽  
pp. 90-97 ◽  
Author(s):  
Chethan K. Gaddam ◽  
Chung-Hsuan Huang ◽  
Randy L. Vander Wal

Biomolecules ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1523
Author(s):  
Juliette de Noiron ◽  
Marion Hoareau ◽  
Jessie Colin ◽  
Isabelle Guénal

Apoptosis is associated with numerous phenotypical characteristics, and is thus studied with many tools. In this study, we compared two broadly used apoptotic assays: TUNEL and staining with an antibody targeting the activated form of an effector caspase. To compare them, we developed a protocol based on commonly used tools such as image filtering, z-projection, and thresholding. Even though it is commonly used in image-processing protocols, thresholding remains a recurring problem. Here, we analyzed the impact of processing parameters and readout choice on the accuracy of apoptotic signal quantification. Our results show that TUNEL is quite robust, even if image processing parameters may not always allow to detect subtle differences of the apoptotic rate. On the contrary, images from anti-cleaved caspase staining are more sensitive to handle and necessitate being processed more carefully. We then developed an open-source Fiji macro automatizing most steps of the image processing and quantification protocol. It is noteworthy that the field of application of this macro is wider than apoptosis and it can be used to treat and quantify other kind of images.


Author(s):  
Vittorio Murino ◽  
Gian Luca Foresti ◽  
Carlo S. Regazzoni

This paper proposes a new approach to the problem of intelligently regulating image-processing parameters of a distributed network. The proposed approach is based on two-step probabilistic process: (a) belief updating, which consists in computing a functional cost at each node of the network and, (b) belief maximization, which depends on maximizing this functional cost by using a stochastic optimization algorithm. The architecture of an image processing system, consisting of three modules connected in a chain-like structure, is presented as an example showing the capabilities of the proposed approach. Each module is provided with a priori information about the set of parameters that manage a particular data transformation, and with evaluation criteria to judge data quality and to decide on the parameters to be adjusted. Experimental results obtained by using a digitally controlled camera and lens objective, are presented to show the validity of the proposed approach.


2017 ◽  
Vol 3 (2) ◽  
pp. 199-202
Author(s):  
Markus Reischl ◽  
Andreas Bartschat ◽  
Urban Liebel ◽  
Jochen Gehrig ◽  
Ference Müller ◽  
...  

AbstractHigh-throughput microscopy makes it possible to observe the morphology of zebrafish on large scale to quantify genetic, toxic or drug effects. The image acquisition is done by automated microscopy, images are evaluated automatically by image processing pipelines, tailored specifically to the requirements of the scientific question. The transfer of such algorithms to other projects, however, is complex due to missing guidelines and lack of mathematical or programming knowledge. In this work, we implement an image processing pipeline for automatic fluorescence quantification in user-defined domains of zebrafish embryos and larvae of different age. The pipeline is capable of detecting embryos and larvae in image stacks and quantifying domain activity. To make this protocol available to the community, we developed an open source software package called „ZebrafishMiner“ which guides the user through all steps of the processing pipeline and makes the algorithms available and easy to handle. We implemented all routines in an MATLAB-based graphical user interface (GUI) that gives the user control over all image processing parameters. The software is shipped with a manual of 30 pages and three tutorial datasets, which guide the user through the manual step by step. It can be downloaded at https://sourceforge.net/projects/scixminer/.


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