scholarly journals colocr: an R package for conducting co-localization analysis on fluorescence microscopy images

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7255
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Image Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used technique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straightforward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization measurements. Included in the package is a shiny app that can be invoked locally to interactively select the regions of interest where two proteins are co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub under the GPL-3 license as part of the ROpenSci collection, https://github.com/ropensci/colocr.

scholarly journals colocr: An R package for conducting co-localization analysis on fluorescence microscopy images

2019 ◽  
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used tech- nique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straight forward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization statistics. Included in the package is a shiny app that can be invoked locally to select the regions of interest where two proteins are interactively co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub as part of the ROpenSci collection, https://github.com/ropensci/colocr. Keywords: R package, co-localization, image-analysis, fluorescence microscopy, statistics

scholarly journals colocr: An R package for conducting co-localization analysis on fluorescence microscopy images

2019 ◽  
Author(s):  
Mahmoud Ahmed ◽  
Trang Huyen Lai ◽  
Deok Ryong Kim
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
R Package ◽  
Regions Of Interest ◽  
Biological Research ◽  
Microscopy Image ◽  
Shiny App ◽  
Localization Analysis ◽  
Microscopy Images ◽  
Selection Of

Background The co-localization analysis of fluorescence microscopy images is a widely used tech- nique in biological research. It is often used to determine the co-distribution of two proteins inside the cell, suggesting that these two proteins could be functionally or physically associated. The limiting step in conducting microscopy image analysis in a graphical interface tool is the selection of the regions of interest for the co-localization of two proteins. Implementation This package provides a simple straight forward workflow for loading fluorescence images, choosing regions of interest and calculating co-localization statistics. Included in the package is a shiny app that can be invoked locally to select the regions of interest where two proteins are interactively co-localized. Availability colocr is available on the comprehensive R archive network, and the source code is available on GitHub as part of the ROpenSci collection, https://github.com/ropensci/colocr. Keywords: R package, co-localization, image-analysis, fluorescence microscopy, statistics

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

scholarly journals Segmentation of Total Cell Area in Brightfield Microscopy Images

2018 ◽  
Vol 1 (4) ◽  
pp. 43 ◽  
Author(s):  
Martin Čepa
Keyword(s):  
Open Source Software ◽  
Total Cell ◽  
Morphological Operations ◽  
Cell Area ◽  
Simple Method ◽  
Microscopy Image ◽  
Fluorescence Images ◽  
Microscopy Image Analysis ◽  
Microscopy Images ◽  
Channel Image

Segmentation is one of the most important steps in microscopy image analysis. Unfortunately, most of the methods use fluorescence images for this task, which is not suitable for analysis that requires a knowledge of area occupied by cells and an experimental design that does not allow necessary labeling. In this protocol, we present a simple method, based on edge detection and morphological operations, that separates total area occupied by cells from the background using only brightfield channel image. The resulting segmented picture can be further used as a mask for fluorescence quantification and other analyses. The whole procedure is carried out in open source software Fiji.

scholarly journals 3D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images

2008 ◽  
Vol 20 (8) ◽  
pp. 1899-1927 ◽  
Author(s):  
Yong Zhang ◽  
Xiaobo Zhou ◽  
Ju Lu ◽  
Jeff Lichtman ◽  
Donald Adjeroh ◽  
...  
Keyword(s):  
Morphological Changes ◽  
Three Dimensional ◽  
Morphological Properties ◽  
Microscopy Image ◽  
Branching Patterns ◽  
Confocal Fluorescence ◽  
Neuronal Processes ◽  
Microscopy Image Analysis ◽  
Difficult Part ◽  
Microscopy Images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.

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