selecting cells using threshold in imagej

Selecting cells using threshold in imagej full#
Selecting cells using threshold in imagej trial#

I will only leave it unchecked in cases where I expect to have distinct pieces of tissue that I need to combine the measurements from.ĭelete existing objects does just what it says, and I do not usually use this as I will simply run a clearAllObjects() command prior to the thresholder if I need to do this.Ĭreate objects for ignored classes is a workaround for all classes like Ignore* that contain an asterisk. Splitting the objects is a great option if you might need to delete one or more created objects at a later point. I usually enable Split objects, which will create one independent object per contiguous outline. Increasing the Min object size effectively removes small objects, while increasing the min hole size effectively removes small holes (fills them in). The Create objects dialog allows you to choose whether to make annotations or detections, the Minimum object size (usually in microns unless pixel size information is missing) below which objects will not be created, the Minimum hole size below which holes will be filled in, and several toggles. If you want the percentage of your tissue that is positive for DAB, this is the quickest way to get that.Ĭreate objects: Create objects both requires a target to operate on, like Measure, and several settings in the Create objects dialog that is shown (image, right) after selecting an area to operate in. Those measurements will be areas and, if more than one class is present, percentages. Measure: Measurements can be saved either to individual objects - including cells - or saved to the image itself. The classifier itself will be saved in the Project folder, within the Classifiers folder, and then within the pixel_classifiers folder. You can also enable them without saving the classifier (if you are craaaaaaazy and do not have a project) using the three vertical dots button in the lower right corner. Once the classifier is named and saved, the last three options unlock. You could exclude black marks at the edge of the slide or from the coverslip in the initial thresholder or pixel classifier, and then look for dark DAB objects only within an already created tissue annotation. Region: Where within the image should the thresholder search? Not really applicable for the purposes of an initial tissue annotation, but can be useful for later thresholding steps. I will usually create a new class “Tissue” via the Annotation tab for this step.

Selecting cells using threshold in imagej trial#

This step will likely involve some trial and error, and will be highly sample/scanner/maintenance dependent.Ībove and Below threshold: Where do you want to create your objects? If you want an object for the background, include a classification for it, otherwise leave it blank or Unclassified or as Ignore*. Threshold: There is very little I can recommend here that was not already covered in earlier steps, so I will only emphasize that you can use the number keys or Brightness&Contrast window to look for good values before you start guessing. Conversely, for the color deconvolved channels like Hematoxylin, Residual, or DAB/Eosin, the Above threshold line will be used. For tissue detection, this means using one of the first set of channels will require setting a class for the “Below threshold” class, as that will be the darker part of the image.

The color deconvolved channels are in the opposite direction, with 0 being white/background, and higher values (say around 3+) being closer to black. The key to the channel value is that the Red, Green, Blue, Averaged, Min, and Max channels are all on a 0-255 scale with 0 being black and 255 being white/background. It only matters a little bit what the stain actually is, some stains like DAB absorb/scatter in all three channels and therefor will appear darker in averaged channels. Averaged channels is a measure fo overall stain intensity in that it will be low where there is a lot of staining, and high where there is background. For brightfield images I most commonly use Averaged Channels, though the stain vectors can also be useful here. Lower resolutions will run faster and can be useful for getting a feel for your chosen channel.Ĭhannel: Choose the channel to Threshold on. The 64x downsample means your image will be treated as a bunch of squares 64 pixels on a side, with the average value of all of those pixels (in red, green, and blue, for brightfield).

Selecting cells using threshold in imagej full#

Resolution: As long as your image has pixel size metadata, these values will start with Full for the base resolution to 64X downsample for Very Low.