![]() For the adaptive or per-object methods, this value is the mean of the local thresholds. FinalThreshold: For the global threshold methods, this value is the same as OriginalThreshold.OriginalThreshold: The global threshold for the image.Count: The number of primary objects identified.Lower right: A table showing some of the settings selected by the user, as well as those calculated by the module in order to produce the objects shown.Īvailable measurements Image measurements:.If you need to change the color defaults, you can make adjustments in File > Preferences. Yellow: Discarded due to touching the border.Each object is assigned one of three (default) colors: Lower left: The raw image overlaid with the colored outlines of the identified objects.It is important to note that assigned colors are arbitrary they are used simply to help you distingush the various objects. Upper right: The identified objects shown as a color image where connected pixels that belong to the same object are assigned the same color ( label image).Once the module has finished processing, the module display window will show the following panels: See the section "Available measurements" below for the measurements that are produced by this module. What do I get as output? A set of primary objects are produced by this module, which can be used in downstream modules for measurement purposes or other operations. Provides more detailed information on the setting. ![]() Indicates a condition under which a particular setting may not work well.Our recommendation or example use case for which a particular setting is best used.The following icons are used to call attention to key items: What do the settings mean? See below for help on the individual settings. See the ClassifyPixels module for more information. Since this new image satisfies the constraints above, it can be used as input in IdentifyPrimaryObjects. The result of ClassifyPixels is an image in which the region that falls into the class of interest is light on a dark background. Then, the ClassifyPixels module takes the classifier and applies it to each image to identify areas that correspond to the trained classes. You first train a classifier by identifying areas of images that fall into one of several classes, such as cell body, nucleus, background, etc. If you have images in which the foreground and background cannot be distinguished by intensity alone (e.g, brightfield or DIC images), you can use the ilastik package bundled with CellProfiler to perform pixel-based classification (Windows only). If you are working with color images, they must first be converted to grayscale using the ColorToGray module.If the objects in your images are dark on a light background, you should invert the images using the Invert operation in the ImageMath module.If this is not the case, other modules can be used to pre-process the images to ensure they are in the proper form: The foreground (i.e, regions of interest) are lighter than the background.What do I need as input? To use this module, you will need to make sure that your input image has the following qualities: See the IdentifySecondaryObjects module for details on how to do this. For these reasons, cell bodies are better suited for secondary object identification, since they are best identified by using a previously-identified primary object (i.e, the nuclei) as a reference. In addition, cells often touch their neighbors making it harder to delineate the cell borders. In contrast, cells often have irregular intensity patterns and are lower-contrast with more diffuse staining, making them more challenging to identify than nuclei. ![]() These qualities typically make them appropriate candidates for primary object identification.
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