@jni, yeah redpil does alot of reading the metadata and builds the infrastructure you would need to support these images.
I actually think I implemented reading boolean images. I have a test for it:
https://github.com/hmaarrfk/redpil/blob/master/redpil/tests/test_bmp.py#L76
I actually think I implemented reading boolean images. I have a test for it:
https://github.com/hmaarrfk/redpil/blob/master/redpil/tests/test_bmp.py#L76
The hard thing to understand is that boolean images are forced to be palletted, so even though there are only 2 possible values, those values will have RGB values assigned to them.
but the test also seems to indicated that pillow is also able to read the same image
I think to displace Pillow would take a lot of work. Putting some experimental code in imageio would be dangerous. Originally, i thought there would be a speed advantage. And there was, but I submitted some patches to imageio that helped in that regard. Now redpil supports a different subset of bmp than Pillow so that might be a plus. There were a few examples in some BMP library that I wanted to get to really complete coverage of the BMP standard.
That is an issue I opened up in Pillow for them to expand support for bmp
@hmaarrfk thanks for your link. Looks like a nice place to contribute experimental code :).
Have you seen this early-alpha GitHub Workflow thing - https://user-images.githubusercontent.com/527589/46967252-9d15c200-d075-11e8-886f-19af1cf545fd.gif ? Perhaps, we should think about moving our infra (CI backend, releasing, ASV hooks, etc) to this platform once it's made public.
I have loaded CT dicom images. I want to segment the tissue within the CT. E.g. I want to determine there is 30% soft tissue 45% bone tissue, 20% air etc ... based on hounsefield units.
Anyone an expert in biomedical image analysis with scikit image?
Any useful libraries?
Looking for help. Thanks ☺️
@ewuramaminka I would encourage you to start a thread on https://forum.image.sc with an example of your images, and tagged with
scikit-image.
for a frame of image slices --> fig, axes=plt.subplots(nrows=2, ncols=36) how can I maximize the frame ? I have tried ==> mng = plt.get_current_fig_manager()
mng.resize(*mng.window.maxsize()) but i get AttributeError: 'FigureManagerBase' object has no attribute 'window'. please assist.
mng.resize(*mng.window.maxsize()) but i get AttributeError: 'FigureManagerBase' object has no attribute 'window'. please assist.
In either case, this seems to be as challenging task
https://stackoverflow.com/questions/12439588/how-to-maximize-a-plt-show-window-using-python
https://stackoverflow.com/questions/12439588/how-to-maximize-a-plt-show-window-using-python
@ewuramaminka for processing medical CT images using skimage, a great starting resource is the Data Science Bowl 2017 with a large number of Jupyter Notebooks (called Kaggle Kernels) on how to preprocess the data, segment lungs, and ultimately train models: https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial and https://www.kaggle.com/ankasor/improved-lung-segmentation-using-watershed are a good start. They also deal directly with the DICOM data and show how that can be opened, rescaled, etc. The kernels page is here: https://www.kaggle.com/c/data-science-bowl-2017/kernels and the competition also has a large number of discussions which can provide useful starting points.
and yes, no integers larger than 65535 (numpy array represents 16-bit grayscale data)
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I used nibabel to load it fine but it’s a 3D and I threshold it using threshold_otsu
The error I’m getting is “Nifti1Image’ object has no attribute min
Please assist
Nevermind, I see that it is not.
@ewuramaminka I got it working by opening the image through ImageJ using pyimagej. :-)
This gets you a numpy array from your NIFTI data, which is presumably what you want? Then you can analyze it with skimage (and/or ImageJ) as per usual.
You mention nibabel above… your error suggests to me the object you get back isn’t an numpy array, but a
Nifti1Image object. There is probably a function you can call on it that will give you a numpy array? Sorry, I don’t know that library.
But it sounds like you have other/better ways of getting the data into Python already. Particularly
nibabel which looks pretty tailored to your needs?
It’s just a matter of getting the pixels as a numpy array, right?