Next we must run the Analyze Particles tool in Fiji to locate patches of white pixels and count them. Now you have a set of white foreground patches of white pixels, surrounded by black background pixel areas, and we have separated touching objects with a watershed, which put “dams” one pixel wide between the objects. This method only works robustly for roughly circular objects. Notice how the nuclei have been split away from each other. Your watershed image should look like this: Where 2 “Watersheds” meet, it builds a dam to separate them! One could do all these steps manually, but the watershed function automates that for you, which is nice. This method finds the centre of each object (using a morphological erode operation), then calculates a distance map from the object centre points to the edges of the objects, then fills that “topological map” with imaginary water. To run the built in ImageJ watershed method choose menu item: Process - Binary - Watershed. Watershed algorithm - separate touching objects We will use the watershed method built into Fiji for that: Also it is clear that some nuclei are connected to adjacent ones… and we want them to be separated. So far so good… But we still don’t have objects… only background and foreground pixels. Black is background, and white is foreground. If you are happy with the automatically calculate threshold, then click “Apply”, which will give you a binary image. Indeed, in this case the background is dark! The default method will be previewed automatically when you launch the menu command, and a threshold will be set, something close to 100 intensity. Turn on the check box for “Dark background”. You might get a different answer in the end!ĭo menu command Image - Adjust - Threshold. You can play with different methods if you like. In this case the default method works pretty well, but you can see there is a long list of methods, which give slightly different threshold results for this image. Fiji has a number of built in Automatic Thresholding methods that try to distinguish the background from the foreground. Next we need to separate the objects from the background using pixel intensity thresholding.Pixel Intensity Threshold - find the foreground areas You should get an image that looks like this: You can preview other values to see how they look also. Run menu command: Process - Filters - Gaussian Blur, with a sigma value of 3 pixels. Too high a sigma value, and the objects will be too blurred, making it harder to find their edges precisely and separate them later. A value of sigma too small will mean that the segmentation will be disturbed by the noise and staining pattern. We will use a large sigma value of 3 for this task. Run a Gaussian Blur filter on the image to blur out the “speckle”, actually Poisson distributed, statistical “photon shot noise”, and also to smooth out the inhomogeneity of the nuclear staining. Open the sample image of touching DAPI stained cell nuclei from a confocal laser scanning microscope. Okay, so how can we denoise, segment, watershed (separate touching objects) and then count / measure the objects in Fiji? Read on….
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |