![]() More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. It is typically used to locate objects and boundaries. Image segmentation is “the process of partitioning a digital image into multiple segments.” ( Wikipedia) Run("Enhance Contrast.", "saturated=0.01") //enhances the contrast linearly with a minimal saturation (optimally this can be set to 0.See this helpful workshop on Image Segmentation for another great overview of Segmentation! Introduction ![]() Here an example with the respective macro code: run("Pseudo flat field correction", "blurring=5") //needs to be adjusted according to image and object size.Ĭlose() //closes the artificial background image created by the plugin Follow these instructions to install this plugins. In the case of a pseudo-flatfield correction you can have a look at that method in the BioVoxxel toolbox which includes the necessary steps and is straight forward to use. This is what you see in the example of I would just omit the 8-bit conversion in the end in the latter example. A normalization is rather the proper enhancement if necessary at all. Furthermore, an histogram equalization is scientifically not recommendable in the case of image visualization because it is a non-linear method and does NOT treat different pixels to the same extent (depending on there initial intensity). Intensity correction is primarily done by a multiplication of every pixel with the average background intensity to end up in the former range of intensities instead of a contrast enhancement. Therefore, a division is normally better. In cases of brightfield or DIC images it is not recommendable to subtract a background image, because it might lead to the elimination of information in areas where the background is higher than the same area in the actual sample image. The suggestion given by includes 2 parts which lead to a suboptimal outcome. So, it needs to be done carefully and might not be applicable in all situations (depending on what you want to do with the image afterwards). Nevertheless, this can lead to changes of the image content, because it is an artificial method. ![]() Then a pseudo-flat field correction might help. This will not help, if the unevenness comes from within the sample because it will be different in every image. Here 2 links to some explanations on this 1, 2. This may serve as a background during correction if the unevenness in the image comes from an uneven lighting (in many cases due to missing Koehler setup of the microscope. It may help to acquire a background image during image acquisition (one in which you only image your slide or dish without any sample but only embedding medium or the solution you use for your sample). If it is coming from something swimming in the solution, then potentially it is more difficult to deal with but it should be considered to solve it before imaging. The question is, are the dark areas coming from something out of focus below or even on the slide or dish which is imaged? Clean coverslips, slides and an objective lense are of foremost importance to avoid this. This is most likely in your image not the case if you just want to get a more even background but still it is important to think about the outcome. What mostly happens is that the content of the image changes and therefore, it might happen that the scientific content changes to some extent. You cannot improve scientific image quality with image processing methods, like image filters, contrast, etc. Second, there is a lot one should consider before applying post-processing techniques on images. The lower the value, the harder the processing. You can play around with the sigma value and see what the effect is in the final image. ![]() Open the image you want to process and then run the code. Just copy the code, then go to Plugins>New>Macro and paste it into the window that opens. ImageCalculator("Divide create 32-bit", "generic1","blur") Run("Gaussian Blur.", "sigma=7") //change blur radius here Created with ImageJ 1.45j10 by Christine Labno This macro is for pseudo-flatfield correction of DIC images In your image, either one of them seems to work fine. I´ve noticed that divide works better in some cases. It is similar to what was suggested by but uses “divide” instead of “subtract”. You can also try this code I found browsing the web a while ago.
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