# Image Basics ### Overall Goal To get familiar with libraries that mialab [depends on](https://github.com/ubern-mialab/mialab/blob/main/requirements.txt), so you know what the data types and functions/methods that you'll use for the next several weeks look like. Libraries here = [numpy](https://numpy.org), [simpleITK](https://simpleitk.org). Later on, you will also be friends with matplotlib, pillow and scikit-learn. Optionally, if you're very social and like even more friends, you could try nibabel, scikit-image and our homegrown kid, pymia. ### Getting Started This exercise is organized using GitHub Classroom, where every time you commit a change, it automatically runs the test script and evaluates if you did the right thing or not. To attempt the assignment: This should create a private repository for you. Then, you can simply open the script image\_basics.py and then edit it, all within the browser or clone to work on it. {% hint style="info" %} Note that you'll have to change the location of the repository from \*/ubern-mialab/\* to \*/\/\* in the above links, once you have forked correctly. {% endhint %} If you think you've made the right changes to the file, feel free to then \*commit\* the change, using the green "commit changes..." button if you are using the browser, and don't forget to include a helpful message about what the change really is. Then, wait for the cogwheels to run, GitHub runs it's 'actions' behind the scenes, and you'll either see a red ❌ or a green ✅ next to the commit message you entered previously. The goal really is to get the green ✅ every time you make a change, which will only happen when all the functions are completed correctly (correctly as defined by the tests in test\_image\_basics.py, please don't cheat ;-)). Clicking on the check mark will show more details about completion of the homework. Once you're done, please share a link to your fork with us, so we can confirm you've completed the assignment for the bonus grade (if such a need arises). This is the first time we set up every exercise using GitHub classroom, so do not hesitate to contact us on Slack if you meet any trouble. ### Tasks and specifics load\_image - load an image using the SimpleITK python interface, and return the pixel data based on if it is a label or not. to\_numpy\_array - transform the SimpleITK image to a numpy ndarray. to\_sitk\_image - transform the numpy ndarray to a SimpleITK image (the reverse of the previous function) preprocess\_rescale\_numpy - rescale the intensities of the np\_img to the range \[new\_min\_val, new\_max\_val]. preprocess\_rescale\_sitk - rescale the intensities of the img to the range \[new\_min\_val, new\_max\_val], but this time, using SimpleITK register\_images - execute the registration\_method to the img (hint: fixed=atlas\_img, moving=img); see helpful comments within the function body here: extract\_feature\_median - apply median filter to image postprocess\_largest\_component - get the connected components from the image ### Help? Numpy documentation: SimpleITK documentation: If neither of these two help, reach out to the instructors on Slack. Responses are not guaranteed to be time bound, we will do our best.