Fluorescence microscopy image-analysis (FMI) for the characterization of interphase HO˙ production originated by heterogeneous catalysis

2017 ◽  
Vol 53 (17) ◽  
pp. 2575-2577 ◽  
Author(s):  
Shuo Zhang ◽  
Xie Quan ◽  
Dong Wang
Keyword(s):  
Image Analysis ◽  
Heterogeneous Catalysis ◽  
Fluorescence Microscopy ◽  
Quick Response ◽  
Microscopy Image ◽  
Response Method ◽  
Microscopy Image Analysis

The FMI visually revealed the inhomogeneity and intensity of interphase HO˙-production, performing as a quick-response method to evaluate HO˙-assigned heterogeneous catalysis.

scholarly journals Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

ACS Nano ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 17125-17133
Author(s):  
Byoungsang Lee ◽  
Seokyoung Yoon ◽  
Jin Woong Lee ◽  
Yunchul Kim ◽  
Junhyuck Chang ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Analysis ◽  
Statistical Characterization ◽  
Electron Microscopy Image ◽  
Microscopy Image ◽  
Microscopy Image Analysis

scholarly journals SPOT DETECTION METHODS IN FLUORESCENCE MICROSCOPY IMAGING: A REVIEW

2018 ◽  
Vol 37 (3) ◽  
pp. 173 ◽  
Author(s):  
Matsilele Aubrey Mabaso ◽  
Daniel James Withey ◽  
Bhekisipho Twala
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Research Effort ◽  
Detection Methods ◽  
Microscopy Imaging ◽  
Spot Detection ◽  
Microscopy Image ◽  
Benchmark Datasets ◽  
A Cell ◽  
Microscopy Image Analysis

Fluorescence microscopy imaging has become one of the essential tools used by biologists to visualize and study intracellular particles within a cell. Studying these particles is a long-term research effort in the field of microscopy image analysis, consisting of discovering the relationship between the dynamics of particles and their functions. However, biologists are faced with challenges such as the counting and tracking of these intracellular particles. To overcome the issues faced by biologists, tools which can extract the location and motion of these particles are essential. One of the most important steps in these analyses is to accurately detect particle positions in an image, termed spot detection. The detection of spots in microscopy imaging is seen as a critical step for further quantitative analysis. However, the evaluation of these microscopic images is mainly conducted manually, with automated methods becoming popular. This work presents some advances in fluorescence microscopy image analysis, focusing on the detection methods needed for quantifying the location of these spots. We review several existing detection methods in microscopy imaging, along with existing synthetic benchmark datasets and evaluation metrics.

scholarly journals Digital removal of autofluorescence from microscopy images

2019 ◽  
Author(s):  
Heeva Baharlou ◽  
Nicolas P Canete ◽  
Kirstie M Bertram ◽  
Kerrie J Sandgren ◽  
Anthony L Cunningham ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Protein Expression ◽  
Supplementary Information ◽  
Link Type ◽  
Microscopy Image ◽  
Microscopy Image Analysis ◽  
Microscopy Images ◽  
Standing Problem

AbstractAutofluorescence is a long-standing problem that has hindered fluorescence microscopy image analysis. To address this, we have developed a method that identifies and removes autofluorescent signals from multi-channel images post acquisition. We demonstrate the broad utility of this algorithm in accurately assessing protein expression in situ through the removal of interfering autofluorescent signals.Availability and implementationhttps://ellispatrick.github.io/[email protected] informationSupplementary Figs. 1–13

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