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Comparison of local thresholding methods.

By Jeremy Adler (655530) and Ingela Parmryd (55665)


<p>In A, B and C each panel shows an image plus a graph (white) displaying the intensities along a horizontal line passing through the centre of the objects. The scale is 0–160. (A) Illustrates the creation of the test image from a sequence of circular objects (their diameters in pixels are superimposed) (A1) plus background with an offset and Poisson noise (A2). (B) Thresholds created from the test image using different operations over a calculation area with a diameter of 21 pixels. Note that (B1) and (B3) include a second graph (yellow), showing the primary threshold plus two standard deviations. (C) The consequences of applying six different thresholds to the test image (A2). Intensities above the threshold were retained while those below the threshold were set to zero. (D) Scattergrams showing the effect of the LMT. The source images are 62 pixel wide horizontal strips taken from the centre of the test image (A1), with noise added independently. In the scattergram showing the effects of the LMT dashed ellipses highlight new combination of intensities. (E) The size of objects and the effectiveness of different thresholding methods. Size of objects is expressed as their area as a fraction of the calculation area (diameter of 21 pixels) and effectiveness by the fraction of the area of each object that was correctly thresholded. Intensity and noise as in the test image (A2). Data shown are means +SD, N = 8. Additional analysis is shown in <a href="" target="_blank">Table 1</a>.</p

Topics: Biological Sciences, Negative correlations, intensity, Hcoef, Quantifying Colocalization, fluorophore, Void Voxels, thresholding methods, Phansalkar method, measure correlation, Intensity thresholding, colocalization coefficient, interest e.g, nuclei
Year: 2014
DOI identifier: 10.1371/journal.pone.0111983.g001
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Provided by: FigShare
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