circular hough transform (cht) algorithm implemented in the imfindcircles function of  (MathWorks Inc)

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