Assignment 2 – Volume visualisation
Requirements to pass: Present a working solution to one of the lab assistants (Fredrik or Raphaela) during the lab sessions.
The pdf version for this assignment is found here.
Please also look at the pdf for more info and images!
OBTAINING THE SOURCE CODE AND DATASETS
PART 1 – VISUALISATION OF CT DATA WITH MPR
In this part you will visualize a CT-scan of the abdominal region of a human. You will also visualize a pre-segmented “mask” that shows where in the volume the liver is located. This type of visualisation could for example be used by a physician to verify that the mask, which might have been generated with an automatic segmentation algorithm, is correct. The CT scan, ctscan_ez.vtk, is stored as a vtkStructuredPoints dataset of signed 16-bit data (short) representing Hounsfield units. The segmented liver is represented as a binary 8-bit (unsigned char) volume, ctscan_ez_bin.vtk, where the liver voxels have the value 255 and the background voxels have the value 0.
Applying a contour filter directly to the binary liver segmentation will result in an isosurface with a lot of aliasing. Therefore, you should also compare this visualisation with a visualisation of a pre-filtered (Gaussian smoothed) version of the segmentation, ctscan_ez_smooth.vtk. To see how the smoothed volume was generated, you can take a look at the script gaussian_smooth.py.
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PART 2 – VOLUME RENDERING
In this part you should investigate the volume rendering capabilities of Paraview. You can use the CT dataset from the first part above if you want, or the foot.vtk (CT) or brain.vtk (MRI) datasets provided in the zip-file. The aim is to create a visualisation that displays several structures in a volume by using transparency and different colors. For a CT scan this could be, for example, skin, bone, and fat. For an MRI scan this could be peaks in the image histogram. To solve this task, you need to create a custom transfer function (colormap) in ParaView and fine-tune it for your dataset.
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