Programming Tutorial Course - Exercises for Producing Visual Output
In this tutorial course, Michael Berks provides exercises that involve loading 3D data, displaying output, and visualizing images. Students are tasked with analyzing patient data, computing mean T1 values in tumor regions, identifying slices with the most tumor voxels, and displaying image outputs effectively using Python and MATLAB. The exercises aim to enhance participants' skills in processing medical imaging data and visualizing results.
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Programming tutorial course michael.berks@manchester.ac.uk Exercises 6: Producing visual output
Exercises 1 loading 3D data This is an extension of last week s tutorial Download travastin.zip and unzip into your data folder The data contains 10 patients, each of which has 2 image visits In each visit folder there is: A post-contrast T1-weighted image A quantitative T1 map An ROI mask, with non-zero voxels specifying the tumour Loop through each patient data folder, and compute the mean T1- value (from the quantitative T1 map) in the tumour ROI Hint: The images are 3D Analyze format, so use niftiread in Matlab and the Nibabel module in python Use the loaded ROI mask as a logical (black-and-white) mask as you did for the vessel mask
Exercises 2 displaying output Go through the Travastin patient data again and for each visit Work out the slice with most tumour voxels Hint: use sum over the 1st and 2nd axes of the ROI mask Get the (x,y)-coordinates of the tumour in that slice Hint: use find (with 2 outputs) in Matlab or np.nonzero in python Display the slice in the post-contrast T1-weighted image and plot the location of the tumour voxels with red crosses
Exercises 3 displaying images Go through the script: visual_output.py Make sure understand the examples (the comments should help) Try changing the image/plot properties to create new examples
