For an 8 bit scale image, which has intensity values from 0 to 255, the pixels with a value of 255 might really be 255, or they might all have different higher values, like 450, or 11,345 but you have no way of knowing. If there are too many pixels (or even any!) pixels that have an intensity value at the maximum of the scale, then there is lost intensity information. ![]() This is simply a histogram showing how many pixels in the image have which intensity. The easiest way to detect intensity saturation is to look at the intensity distribution histogram of the image. saturation / over exposure, such that the intensity scale of the information is clipped off at the top, like a mountain with the peak removed… so you can’t tell how high it was. Next we can have a look to see of the image has been acquired or manipulated in such a way that the highest intensity information has been lost, due to detector CCD, PMT, etc. Intensity Saturation or Clipping or Over Exposure: I don’t know why biological imaging thinks its so special…Ģ. This is a big problem in scientific publication in biological sciences, and we need to fix it: We need an online image database where data is published for others to down load and analyse, as is required by scientific publishing in other fields, eg x-ray crystallography for protein 3D structure and DNA/genome etc. Images in PDF files of published research articles are usually lossy compressed, so its hard or impossible to repeat the image processing they did because the image data is messed up. Lossy compression is good for making movies etc smaller in file size, but bad for measurements afterwards. ![]() Never use lossy JPEG or other lossy compression to store scientific data. Some areas have been given all the same intensity (which is unlikely to be true) and some have ugly repetitive patterns, which are clearly false. The image intensity data is messed up… so you would not want to quantify intensity data from an image like that. Can you see that there are square shaped artifacts in the image which are 8x8 pixels? These are lossy JPEG compression artifacts. You should see something like the image below. To Begin with, we should split the three color channel images into separate windows so we can inspect them and manipulate them separately: Select the FluorescentCells.tif image window and do menu item: “Image-Color-Split Channels”.įirst, we can interrogate the red or green channel, and see if there are any lossy compression artifacts visible by eye: Click the magnifying glass icon in the Fiji main window and left click to zoom in to an area of interest (left click zooms in, right click zooms out again). The slider under the image is to change between the three colour channels, and you can see the colour of the border and the writing at the top of the image in the information bar change colour accordingly (the meta data of which channel is what colour was stored in the tiff file, but your images may or may not have that meta data!) Click the image then press “i” to see info about the image. In Fiji, do “File - Open Sample Images - FluorescentCells.tiff, to open a three colour channel “pretty” image of some fixed and stained cells grown on glass. Lets open a sample image and check it out: How can you detect these problems in an image? Worse, you may have some images that you suspect might have been captured in such a way that information was missed or lost at the image capture stage due to bad detector settings. Suppose you are given some images by a colleague, or have some images of your own, and you suspect that they may have been damaged by compression artifacts or brightness/contrast adjustment.
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