![]() # replace pixels with value more than 50 and less than 200 to 0. Unsignedpix = map(lambda p: p & 0xff, signedpix) # converting signed pixel values to unsigned using bitwise operation. Signedpix = imp.getProcessor().getPixels() # ImageProcessor.getPixels() returns an array of signed pixel values The scripts load “blobs.gif” example image from NIH server, then replaces pixels with values between 50 and 200 to 0. Here is another example of going back and forth between signed and unsigned values. The Jython book "The Definitive Guide to Jython": it's saying that the book is a version from 2009, but the latest commit is in Oct. Examples below show how they are actually used in Jython scripts, to save our time for reading the source code of each. ![]() This is because the style how each algorithm is implemented is not consistent (they are written by 1000 different people!) so it takes a while to figure out how to use them when we are writing a bioimage analysis workflow. This page is like a cookbook: there are no details about how to do programming, but more centered on how to use Classes built in ImageJ and its plugins. Recently, there is a very good tutorial page for real beginners: here (UVA Research Computing Learning Portal). The former is in a tutorial style so if you want to learn how to do scripting using Jython, that's the place where you go. for (int i=r1.For learning image processing using Fiji and Jython scripting, go to excellent tutorials written by Albert Cardona, such as here in his website or here in.imagej/imagej1/blob/master/ij/process/FloatBlitter.java#L89 In that code you find the actual operation: This blitter can for example be a FloatBlitter (for processing 32-bit float images). However, the actual operation is done by a so called “Blitter”. If you want to dig a bit in the code to find out what the Image Calculator is actually doing, the Image Calculator code is online. I would raise another question: How do you know that the intensity in both channels is really the same? I assume the channels express intensities which were imaged with a different wavelength, right? How do you justify subtracting an image with wavelength X from an image with wavelength Y? Maybe a reviewer could raise such a question… Thus, in your particular case, 32-bit images should be preferred from a mathematical point of view. However, 8-bit images don’t support negative values and thus, the result would be 0. For example, if you work with 8-bit images and you subtract a pixel with value 8 from a pixel with value 7, the result should be -1. However, it depends a bit on the image type you are working with. The other other option for me is to manually look into the code. If anyone has any deeper understanding of the image calculator I would be most appreciative. the latter determines whether I need to come up with a new analytical strategy or not. Is it literally subtracting the intensity values per pixel from image1 and image2? Or is it removing any pixels that are fluorescing in one image but not in the other according to some threshold? Wether it is doing the former vs. On the documentation for ImageJ, it simply states that an image calculator subtraction is Image = Image1 - Image2, but that doesn’t give me enough information for a publication. Since the final channel (after the “Image Calculations”) is what I will be measuring intensities from for publication, I need to know precisely what the “Image Calculator” does. With the second channel, I subtract the first (generating a new channel with everything BUT the protein of interest), which I then subtract from the original channel. I found a way to remove this non-specific labeling by doing some fancy work with the “Image Calculator”, since another channel also co-labels the same cells. However, there is some non-specific labeling of the antibody I used near the top of the confocal stack, which is not due to the antibody binding to the protein of interest. I want to essentially measure the fluorescence intensities of cells in one channel using the “measure” command via hand drawn contours. I have a few confocal stacks that I need to analyze for a research project. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |