Add a colormap (with a colorbar) that maps the 16-bit CT Hounsfield values to grayscales.The MPR should contain one slice plane per image axis. Add a multi-planar reformatting (MPR) visualisation of the CT volume (using the Slice filter).To see how the smoothed volume was generated, you can take a look at the script gaussian_smooth.py. Therefore, you should also compare this visualisation with a visualisation of a pre-filtered (Gaussian smoothed) version of the segmentation, ctscan_ez_smooth.vtk. 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.Īpplying a contour filter directly to the binary liver segmentation will result in an isosurface with a lot of aliasing. The CT scan, ctscan_ez.vtk, is stored as a vtkStructuredPoints dataset of signed 16-bit data (short) representing Hounsfield units. 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. You will also visualize a pre-segmented “mask” that shows where in the volume the liver is located. In this part you will visualize a CT-scan of the abdominal region of a human. PART 1 – VISUALISATION OF CT DATA WITH MPR Please also look at the pdf for more info and images! OBTAINING THE SOURCE CODE AND DATASETSĭownload and extract the following zip-files ( part1.zip and part2.zip) that contain the datasets for the assignment. The pdf version for this assignment is found here. Requirements to pass: Present a working solution to one of the lab assistants (Fredrik or Raphaela) during the lab sessions.
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