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Fig. 2. Segmentation. (A-D) Segmentation using the simple threshold method. The DAPI channel (B) from the cell shown in A is used for segmentation. The histogram (D) of intensity values for the DAPI channel shows bimodality. A background population of pixels averages an intensity of 20 and a foreground population of pixels averages an intensity of 100. The optimal threshold (an intensity of 50) is the intensity that separates those two population while maximizing the between-population variance and minimizing the within-population variance. (C) Binary image created by using the optimal threshold. (E-L) Segmentation using edge detection. Yeast cells imaged in DIC that are unsuitable for threshold-based segmentation can be segmented by the edge-detection algorithm. (E) Raw image of the yeast cells. Results of an edge detection (F) operation are then improved by morphological dilation (G) and a morphological operation that fills the `holes' in the image. Owing to the initial dilation, the binary masks are too large and are reduced by morphological erosion (I). The results of the segmentation are three unconnected yeast cells that can be labeled individually. Images K and L show the final edge and an overlay of the edge on the original cells. (M-P) Segmentation using watershed operation. Nuclei of Drosophila S2 cells stained with DAPI (M) are in close proximity and a simple threshold (N) does not segment them properly. A distance transformation assigns each pixel inside the cell a value equal to its distance from the edge. This creates an image that can be interpreted as 3D topographical landscape containing two `hills' (O). The watershed transformation separates the hill by a mathematical operation that is equivalent to `raising the water level' until all hills are separated; the result is seen in P.
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