2007
DOI: 10.1002/jemt.20485
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A new detection algorithm for image analysis of single, fluorescence‐labeled proteins in living cells

Abstract: KEY WORDSpeaks over threshold; single-molecule microscopy; single-molecule tracking ABSTRACT A new algorithm is presented for the detection of single, fluorescence-labeled proteins in the analysis of images from living cells. It is especially suited for images with just a few (<1 per 10 lm 2 ) fluorescence peaks from individual proteins with high background and noise (signal to background ratios as low as 2 and signal to noise as low as 10). The analysis uses the peaks over threshold method from extreme value … Show more

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Cited by 16 publications
(15 citation statements)
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References 28 publications
(40 reference statements)
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“…The experimental arrangement for single-molecule imaging has been described in detail previously (35,36). Essentially, the samples were mounted onto an inverted microscope (Axiovert 200; Zeiss, Gttingen, Germany) equipped with a 100 objective (PlanNeofluor 100, N.A.…”
Section: Methodsmentioning
confidence: 99%
“…The experimental arrangement for single-molecule imaging has been described in detail previously (35,36). Essentially, the samples were mounted onto an inverted microscope (Axiovert 200; Zeiss, Gttingen, Germany) equipped with a 100 objective (PlanNeofluor 100, N.A.…”
Section: Methodsmentioning
confidence: 99%
“…Excursion distance is measured as the maximum separation between any two points on the organelle track. The image denoising uses an approach proposed for protein image denoising (Michel et al, 2007) and subsequently applied by us for 2-D organelle images (Chenouard et al, 2014) and 3-D stem cell time lapse images (Wait et al, 2014). This approach models the noise as slow varying background combined with high-frequency shot noise.…”
Section: Methodsmentioning
confidence: 99%
“…Here the HSCs typically appear as dark circular regions within the stromal layer. To detect these circular regions we used Michel's technique to enhance the contrast in the image region [10], segmented the image into foreground and background using Otsu thresholding, and then looked for pixels on the edges of circles using the Circle Hough Transform (CHT) [11]. …”
Section: Methodsmentioning
confidence: 99%