circular hough transformation (cht) algorithm Search Results


circular hough transformation (cht) algorithm  (MathWorks Inc)
90
MathWorks Inc circular hough transformation (cht) algorithm
<t>Automated</t> protein liquid–liquid phase-separated droplet analysis. a <t>CHT</t> circle detection, followed by individual droplet grouping, efficiently detects well-behaved protein droplets from the sample. Blue circles indicate qualified protein droplets detected by the program. The image is 127.3 μm in width and height. b The average fluorescence intensities per pixel within droplets were quantified for the same protein droplets at different fluorescent cargo protein concentrations. The fluorescence signal increases as the cargo concentration increases, although there was only a negligible deference between two data points below 25 nM. Each average was calculated from n > 200 droplets from seven image stacks. Error bars indicate standard deviations between image stacks
Circular Hough Transformation (Cht) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
circular hough transformation (cht) algorithm - by Bioz Stars, 2026-03
90/100 stars
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circular hough transformation  (MathWorks Inc)
90
MathWorks Inc circular hough transformation
<t>Automated</t> protein liquid–liquid phase-separated droplet analysis. a <t>CHT</t> circle detection, followed by individual droplet grouping, efficiently detects well-behaved protein droplets from the sample. Blue circles indicate qualified protein droplets detected by the program. The image is 127.3 μm in width and height. b The average fluorescence intensities per pixel within droplets were quantified for the same protein droplets at different fluorescent cargo protein concentrations. The fluorescence signal increases as the cargo concentration increases, although there was only a negligible deference between two data points below 25 nM. Each average was calculated from n > 200 droplets from seven image stacks. Error bars indicate standard deviations between image stacks
Circular Hough Transformation, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/circular hough transformation/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
circular hough transformation - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


Automated protein liquid–liquid phase-separated droplet analysis. a CHT circle detection, followed by individual droplet grouping, efficiently detects well-behaved protein droplets from the sample. Blue circles indicate qualified protein droplets detected by the program. The image is 127.3 μm in width and height. b The average fluorescence intensities per pixel within droplets were quantified for the same protein droplets at different fluorescent cargo protein concentrations. The fluorescence signal increases as the cargo concentration increases, although there was only a negligible deference between two data points below 25 nM. Each average was calculated from n > 200 droplets from seven image stacks. Error bars indicate standard deviations between image stacks

Journal: BMC Bioinformatics

Article Title: Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

doi: 10.1186/s12859-022-04577-2

Figure Lengend Snippet: Automated protein liquid–liquid phase-separated droplet analysis. a CHT circle detection, followed by individual droplet grouping, efficiently detects well-behaved protein droplets from the sample. Blue circles indicate qualified protein droplets detected by the program. The image is 127.3 μm in width and height. b The average fluorescence intensities per pixel within droplets were quantified for the same protein droplets at different fluorescent cargo protein concentrations. The fluorescence signal increases as the cargo concentration increases, although there was only a negligible deference between two data points below 25 nM. Each average was calculated from n > 200 droplets from seven image stacks. Error bars indicate standard deviations between image stacks

Article Snippet: Hermann et al. introduced the circular Hough transformation (CHT) algorithm for the automated segmentation of GUV images for intensity computation in Matlab [ ].

Techniques: Fluorescence, Concentration Assay