I'm processing a 3D numpy array and would like to know if it is possible to write/read the entire 3D volume as 1 single 3D .dcm rather than a series of 2D .dcm files. Can this be accomplished using pydicom library as this is what I'm using. I'm new in working with DICOMs so I'm not sure how to go about about implementing this if possible.
I found this to be what I wanted but I could not access the code as the link to it is dead. https://github.com/pydicom/pydicom/issues/786
Any help will be appreciated.
Related
how to convert the image into object file like as .obj or .ply . I need some code written in visualization toolkit and c++.
Thanks
Image data is pixel data and .obj/ .ply or for that matter .stl is 3D geometry data with Point and Cell (for .obj Cell is Triangle) information.
Your question is not clear, but to give you some steps -
First, you need to identify how would you convert the pixels into points? vtkImageDataGeometryFilter might be of help here. Although it might not be sufficient as you will also need triangles data.
Once you get vtkPolyData from image data, you can write this data to STL or OBJ or PLY format. You can use following VTK classes for that
vtkSTLWriter, vtkOBJWriter and vtkPLYWriter.
Im new to medical image processing. how can i convert 3D DICOM medical images to numerical matrix format using either python or c++?
Another option, if you really want "3D" dicom image support (ie CT/MR/NM/PET 3d series - as opposed to purely 2D image handling) and you want do anything really 3d related and/or more complex, you might want to check out simple ITK.
That gives you very powerful true 3d handling and is fast (it's wrapped around complied C). It includes, for example, full 3D image registration and various filters/tools etc.
It can read an entire series at once and automatically create a fully spatially aware 3D numpy array for you (ie it takes care of processing all the dicom 3D spatial orientation/spacing etc tags for you)
However, because it's a lot more powerful than pydicom, it also has a much steeper learning curve - but does have many examples and online jupyter notebook tutorials.
...so, depending on your needs it might be good for you. However, if you only really want basic 2d image-at-a-time type processing, pydicom is the way to go.
You can use pydicom package in python. You can install it in python by:
pip install pydicom
Here is a simple example of reading DICOM images and converting to numpy array:
import os
import pydicom
import numpy as np
dicom_dir = your_dicom_folder_of_slices
file_names = os.listdir(dicom_dir)
file_names.sort()
dicom_data = []
for name in file_names:
path = os.path.join(dicom_dir, name)
dicom_data.append(pydicom.read_file(path))
array = [data.pixel_array for data in dicom_data]
array = np.stack(array, axis=-1) # or 0 if 'channel_first'
Here is a detailed example.
I prefer using SimpleElastix for medical image processing. it has many methods for segmentations and many other helpful methods. it is available in both python and C++. In my experience SimpleElastix handled DICOMS and niftis better than other Packages.
Thanks for dropping in here.
I'm currently working on a project, and I'm not that strong with python yet. So I was hoping for some constructive feedback on this question.
I have a dataset containing core samples, all stored with sample id, latitude, longitude, content and other data irrelevant for this question.
Now I've imported this dataset and sliced it as I want it to be. For the images I'm using the rasterio module to open 2 satellite images that covers the region. I'm using the utm module to convert back and forth between latlong->UTM->Pixel values (Which also seems to be throwing me strange coordinates at some points).
Annoyingly enough, the two Sentinel-2 images are cut right across the center of the map.
As I'm doing bounding boxes on top of where the samples are taken, this is a problem as I need to extract 10x10 pixel cut outs of that region. This leads to a lot of the samples not getting a proper cut out.
So I thought why not merge the two images together into one large rectangular bit. But I still need to retain the meta data with the UTM coordinates.
How would you suggest I proceed. Can it be done in an easy way? Is there another angle on this I've overlooked?
Thank you for your time.
I'm not sure I completely understand the question, but if you are simply trying to merge 2 images, have you looked at the command line tool gdal_merge.py?
A very simple example:
gdal_merge.py -o merged_image.tif image1.tif image2.tif
GOAL: I have to create a 3d model of a machine part. I have about 25 images of the same thing taken from different angles.
Progress: I am able to extract the coordinates for a label that is on the machine for most of the images.
Problem: but I have no idea how to proceed. I have read a bit about aero-triangulation, but I couldn't figure out how to implement it. I would really appreciate it, if you could guide me in the right direction.
It would be really helpful, if you could provide your solutions using python and opencv.
Edit: sorry but I cannot upload the code for this one as it is confidential. don't blame me please I am just an intern. Although I can tell that I cropped a template of the label from an image and then used Sift to match that template on all the images to get the coordinates of the label.
If you want to implement things yourself with OpenCV, I would command looking at SIFT (or SURF) features, RANSAC and the epipolar constraint. I believe the OpenCV cookbook describe those. Warning: math involved. And I don't know how to do dense mapping in OpenCV.
I know the GUI program "VisualSFM" that can automatically recreate 3D model from images. It uses SFM and other command line utilities behind the scenes. Since everything is opensource, you could create a python wrapper around the actual libraries (I found https://github.com/mapillary/OpenSfM asking Google). VisualSFM prints the command it calls, so a hacky way could be to call the same commands from python.
If it is a simple shape and you don't want to automate it, it could be faster to model it yourself (and the result could look better). In 1.5 week I managed to learn the basics of blender and to model a guitar necklace: https://youtu.be/BCGKsh51TNA . And I would now be able to do it in less than 1h. How long are you ready to invest to find a solution with OpenCV?
I am trying to render these models in Matlab so that I can sample them and then use their shape for my research work.
My question is that: Is there a way to render 3D models, used in different 3D graphics engines, in Matlab?
I have found something similar but it only loads an obj mesh.
Update:
I have also found an answer which uses OpenGL function in a mex file to access the graphics of a figure and get the depth buffer. However what I am trying to do is to render a model within a matlab figure.
Take a look at:
http://www.openu.ac.il/home/hassner/projects/poses/
I understand that it's still not completely stable, but they're working on it.
Anyway, this lets you render to a matrix and then do whatever you like with it (imshow it, imwrite it, etc.)