5XSA0 - Introduction to Medical Imaging

Goal
Become acquainted with the implementation of image filtering in the spatial and frequency domain, the application of image restoration and color image processing algorithms, finding image attributes (points, edges, etc.) and features, controlling segmentation parameters for obtaining optimal results and gain a basic understanding of machine learning algorithms and their validation.

Contents
Medical imaging is the most important discipline for healthcare diagnosis and intervention. This course provides in-depth knowledge on medical imaging techniques and the principles of the medical image analysis. This course starts with an introduction to sampling and the extension to multi-dimensional signal processing for images and video. Then the course proceeds with the principles and definitions for the frequency domain representation of images and possibilities for filtering and other processing in the frequency domain. In a further module, the focus is on the image quality enhancement in various ways. Various noise models are discussed and filtering approaches for reducing the noise. The introduction of color in medical images for improved understanding is addressed. The second part of the course provides an introduction to image understanding and object/area segmentation to learn imaging for medical diagnosis. This part starts with simple binary operators and then extends to the detection of points, lines, edges, etc. Secondly, specific region-based segmentation methods are discussed. Features are extracted from images, which is addressed in both the spatial domain directly in the image and a frequency-oriented domain, such as the Gabor transformation. The course concludes with a module on imaging techniques and use cases, where the learnt techniques are applied. These cases include computer-aided cancer diagnosis, instrument detection, etc.

Preknowledge
5ESA0 Signal processing basics

Full schedule: 
Date Time Room Content
24 apr 08.45-10.30 Flux 0.01 Module 1 - motivation, image fundamentals and signal transforms - part 1
24 apr 10.45-12.30 Flux 0.01 Computer class - digital images in MATLAB
1 mei 08.45-10.30 Flux 0.01 Module 1 - motivation, image fundamentals and signal transforms - part 2
1 mei 10.45-12.30 Flux 0.01 Computer class - exercises
4 mei 13.45-15.30 AUD 2 Module 2 - discrete Fourier transform and filtering - part 1
4 mei 15.45-17.30 AUD 2 Computer class - spatial filtering of digital images
8 mei 08.45-10.30 Flux 0.01 Module 2 - discrete Fourier transform and filtering - part 2
8 mei 10.45-12.30 Flux 0.01 Computer class - exercises
11 mei 13.45-15.30 AUD 2 Module 3 - image enhancement - part 1
11 mei 15.45-17.30 AUD 2 Computer class - frequency domain processing
15 mei 08.45-10.30 Flux 0.01 Module 3 - image enhancement - part 2
15 mei 10.45-12.30 Flux 0.01 Computer class - exercises
18 mei 13.45-15.30 AUD 2 Computer class - restoration and colors
18 mei 15.45-17.30 AUD 2 Computer class - exercises
22 mei 08.45-10.30 Flux 0.01 Module 4 - features - part 1
22 mei 10.45-12.30 Flux 0.01 Module 4 - features - part 2
29 mei 08.45-10.30 Flux 0.01 Module 5 - segmentation - part 1
29 mei 10.45-12.30 Flux 0.01 Computer class - features
1 jun 13.45-15.30 AUD 2 Module 5 - segmentation - part 2
1 jun 15.45-17.30 AUD 2 Computer class - exercises
8 jun 13.45-15.30 AUD 2 Computer class - segmentation
8 jun 15.45-17.30 AUD 2 Computer class - exercises
12 jun 08.45-10.30 Flux 0.01 Module 6 - classification - part 1
12 jun 10.45-12.30 Flux 0.01 Module 6 - classification - part 2
15 jun 13.45-15.30 AUD 2 Module 7 - medical imaging modalities - part 1
15 jun 15.45-17.30 AUD 2 Computer class - classification
19 jun 08.45-10.30 Flux 0.01 Module 7 - medical imaging modalities - part 2
19 jun 10.45-12.30 Flux 0.01 Recap and questions
22 jun 13.45-15.30 AUD 2 Computer class - exercises