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This course covers a selection of the most used image analysis methods, with emphasis on images from medical and biological modalities, from microscopy, to ultrasound, MRI, or CT, including time sequences. We describe some of the most used algorithms for solving the key problems in this area - preprocessing, segmentation, registration, reconstruction and classification - and their use in applications. We show how to deal with the specifics of medical data such as non-linear transformation, 3D data, large variability, lack of reliable keypoints, lack of labeled training data etc. This course complements the Computer Vision Methods course, which covers techniques for images from standard optical cameras.
This course assumes the knowledge of basic image processing algorithms, as taught for example in the Digital image course. We assume the knowledge of programming, mathematics and machine learning approximately at the level of the bachelor programs here at FEE (FEL). The knowledge of medical image acquisition devices principles (taught for example in the course BEAM33ZSL) is useful but not necessary.
Lecturer: Jan Kybic (kybic@fel.cvut.cz), consultations by appointment.
Lab assistant: Denis Baručić (barucden@fel.cvut.cz), consultations by appointment.
In the lectures, we will present a number of relevant algorithms for (bio)medical image processing based on the original scientific papers. You are strongly advised to read the papers in advance. Feel free to ask questions, both in advance and during the lecture. The selection of papers can be adapted to some extent, so if there is a particular topic that you are interested in or not interested in at all, let us know.
The Labs will take place in a computer laboratory. In the first part of the semester, you will try to implement some simple algorithms, using available libraries for the more complicated methods. During the second part of the semester, you will work on your Semester work. The labs will also include ten short, graded quizzes related to the papers discussed at the lectures.
The Semester work should take you about 10 hours. It will consist in an independent implementation of some biomedical image analysis algorithm or its application to a particular problem. You are encouraged to choose your own topic, so please approach us if you have an idea. Otherwise, you will be assigned a topic. You are expected to submit a short report (5-10 pages including images) and your code. You should also present your results briefly (5 minutes + equations) to your peers. The presentation can be short but it should explain what the problem was, how others solved it, how you solved it, and how well it worked. The presentation should be understandable for your peers. The report should be structured similarly but with more details, equations, images, and graphs. The code should be commented.
The exam will be oral and in person (epidemiological situation permitting) and the student should demonstrate the knowledge of the basic principles of the discussed algorithms. The (tentative) exam dates are be 15.1., 24.1. and 14.2.
The students are awarded points for the quizzes, semester work, and oral exam. The following table lists the maximum number of points for each graded unit.
To attend the oral exam (get a “zápočet”), one needs to get at least half the points together from the quizzes and the semester work (30/60).
The minimum requirement to pass the exam is to earn half the points (20/40). Assuming this requirement is met, the final grade will be determined based on the sum of all points earned according to the following scale.
This course allows the use of artificial intelligence (AI) tools such as ChatGPT, Google Bard, Github Copilot, and others. However, any AI-generated output (or anything based on AI-generated output) must be clearly denoted and acknowledged in any submitted work. Failure to properly acknowledge the sources of any submitted work will be considered plagiarism.
Students must understand and be able to explain all parts of their submitted work.
The goal of the course is for students to learn and this goal must be respected when employing any AI tools. Consider using AI as a feedback generator or a personal tutor.