As the Christmas break approaches and the Autumn term will soon be over, I am glad that I've been given the opportunity to feature on this blog the teaching material for the course
Medical Image Computing that was newly introduced this year at Imperial College. This course, taught by
Prof. Daniel Rueckert and
Dr. Ben Glocker, aims to provide MSc students with the necessary skills to carry out research in medical image computing: visualisation, image processing, registration, segmentation and machine learning. The lectures were accompanied by tutorials in the form of IPython notebooks developped by Ozan Oktay, using SimpleITK to process medical images in Python and scikit-learn for Machine Learning. These tutorials are made available on
github. They provide an introduction to medical imaging in Python that complements SimpleITK's official
notebooks.
There are
4 tutorials:
- Basic manipulation of medical image, image filtering, contrast enhancement, and visualisation
- Image registration, multi-modal registration, Procrustes analysis
- EM segmentation and gaussian mixtures models, atlas prior, Otsu thresholding
- Machine learning: classification, regression and PCA.
Image registration is the process of aligning images (rigid registration) and warping them (non-rigid registration) in order to compare or combine images. A typical application is a patient being scanned twice at a few months interval and the two scans are registered in order to assess the evolution of a disease. Another application illustrated below (see
tutorial 2) is a patient having an MRI and a CT scan, each modality highlighting different characteristics of a patient's anatomy, and a registration process is required before the doctors can overlay both images.
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Image registration:
the CT scan (red) and the MRI scan (green) are registered in order to be combined in a single image.
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