![]() (This could be a result of the characteristics of the DAB absorbance.) Let’s see …Īfter converting your original images A and B to RGB stacks it can be seen that the strongest effect of the different staining itensity is visible in the green and blue channel. I assume that there is a more simple solution. ![]() I also would like to skip absorbance, stain vector, color deconvolution etc. Let’s do the QuPath processing in the next step. Since it is unclear to me what you want to measure exactly I would like to use ImageJ/Fiji first. In the following I have downsampled the images to half of their original size to reduced the negative effects of this patterns. This checkerboard patterns can be found all over both images and disturbing. Would it be possible to have access to the original images? This can be also seen if the aR|aG|aB absorbance values are displayed in a 3D viewer. This shows that the linear assumption is not valid. The absorbance ratio images should ideally (assuming Bouguer-Lambert-Beer and linear relation between absorbance and concentration) structureless with only a constant value.It would be interesting to know how this image has been created. It shows that image B contains some ‘artefacts’ of unknown source. It is more something like a change of staining intensity which can have various causes.Īnd … have a look onto image aG_divided_by_aR (of image B): The strong staining in image B is not what is usually called ‘background’. The white (transmitted light ) background is similar in A and B. Both images A and B or not perfectly white balanced but very similar. In this transmitted light image the background is bright and the signal is colored and darker than the ‘white’ background. The approach to use different stain vectors and apply color deconvolution to perform a kind of ‘background’ subtraction or normalization is questionable. The mean can be calculated in difference Rois.Īll measurement shows that the stain vectors for A and B are very close to equal. The mean of this absorbance ratio images will show the relation between aR|aG|aB. I have compared the color vector from image A and B in ImageJ: Color deconvolution doesn’t make sense for a single stain. I can’t be sure either of these will give the results you need, but they are the options I would try. It’s most likely to be useful if the ‘true’ structures are all similarly sized, and either blob-liked or at least quite thin.Īlternatively, you can use pixel classification and select multiple features – trying to train QuPath to effectively ignore the background. Cell detection offers this.įor thresholding, there is a ‘Prefilter’ option the Laplacian of Gaussian selection will effectively smooth the image and give it a mean of zero. However, background subtraction can be wrapped up into other commands that operate on part of an image at a time. This allows it to work with much larger images efficiently, since it can always return to the file to request the pixels if it needs to. Hi QuPath is quite different from ImageJ in some fundamental ways – the most important one here is that you can’t change the pixel values in QuPath (e.g.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |