![]() Empirically, we've found that a two-class Otsu threshold works well for this data. This identifies a pixel intensity value to separate the foreground (nuclei) from the background. ![]() This number was chosen empirically: it is smaller than the diameter of a typical nucleus it is small enough that nuclei aren't merged together, yet large enough to suppress over-segmentation of the nuclei.Īdd an Threshold module. A median filter will preserve boundaries better than other smoothing filters such as the Gaussian filter. DNA is not uniformly distributed throughout the nucleus, which can lead to holes forming in the downstream object identification. A median filter will homogenize the signal within the nucleus and reduce noise in the background. Choose a value of 0.5.Īdd a MedianFilter module. Final segmentation results will be minimally affected by downsampling if the objects of interest are relatively large compared to the pixel size. Often, downsampling an image can yield large performance gains and at the same time smooth an image to remove noise. Processing 3D images requires much more computation time than 2D images. When using rescaling in your pipelines, be careful to perform measurements on the original images, not the rescaled images.Īdd a Resize module. In this case, we find that rescaling improves the thresholding and subsequent segmentation of nuclei. Rescaling the DNA image proportionally stretches the intensity values to the full intensity range, from 0 to 1. In this case, Channel 0 contains images of the plasma membrane, Channel 1 contains images of mitochondria, and Channel 2 contain images of DNA.īefore attempting to segment the cells in the images, conditioning the images with filters and various image processing methods will improve the results.Īdd a RescaleIntensity module for the DNA channel.
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