Course syllabus

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Course-PM

SEE120 SEE120 Medical image processing lp4 VT22 (7.5 hp)

Course is offered by the department of Space, Earth and Environment

Contact details

Examiner

John Conway john.conway@chalmers.se

Teachers

John Conway john.conway@chalmers.se

Kirsten Kraiberg Knudsen  kirsten.kudsen@chalmers.se

Teaching Assistant

Michael Olberg  michael.olberg@chalmers.se

 

Course purpose

This course uses examples with medical image processing to teach fundamental knowledge about two and three-dimensional signal processing while giving students a chance to develop their programming skills via project work. More specifically the course teaches the main techniques of Image Processing needed to prepare medical (and other) images for human interpretation or subsequent automated image analysis. These methods respectively improve subjective image quality  (image enhancement), remove known image distortions such as blurring effects (image restoration), reduce image data sizes for storage or transmission (image compression) or form images from indirectly sampled data; such as from projections (image reconstruction). On completion of the course  students should be able to implement simple customized versions of the major image processing algorithms used in medical image processing via coding in MATLAB. Although medical applications of image processing will be emphasized, and most examples will be taken from medicine,  some applications of the techniques in other fields will also be presented.

Schedule

TimeEdit

Course literature

The course textbook is 'Digital Image processing' – Gonzalez and Woods 4th edition, Global edition. Copies are available to purchase at the Chalmers bookstore,

Course design

The course consist of lectures, problem classes, in-class computer exercise sessions and a project executed in pairs with a report to be handed in.  There are usually three two hour instructions classes per week. Hands-on computer exercises will be mixed in during these classes with formal lecturing. Additional to these on  most Tuesday mornings there is a two hour consultation session where students can meet the teachers one-on-one or in pairs to answer questions about topics in the course to and get advice on their project work-

The projects generally involve processing a medical image using software written in MATLAB (or optionally Python).

Learning objectives:

Visualise via means of mental images the process of forming 1D and 2D Fourier transforms and the convolution process. Be able to quantify and explain to others the  practical effects on imaging of using the Discrete Fourier Transform (aliasing etc) and be able to implement methods for eliminating such effects (image padding etc).

Apply knowledge about the human vision system to implement image enhancement methods for human end use. Choose and apply appropriate image enhancement methods for different applications. Discriminate between cases where automated image enhancement methods produce appropriate results and where they do not.

Choose appropriately between averaging and median filtering for reducing image noise based on noise statistics.
Code and apply image smoothing and sharpening techniques to images using both image and Fourier domains methods. Be able to select between optimum methods of edge detection for different applications.
Compute manually the convolution of matrices representing images with point spread functions (PSFs).  Estimate the tradeoff  between improved image sharpness and increased noise on applying different image restoration algorithms and appropriately choose which method to apply in specific cases.
Explain to others the nature of wavelets and be able to apply wavelets to de-noise images.
List common image formats used in medicine.  Be able to justify the use of lossless versus lossy compression for different applications.
Describe and implement in software simple methods of image registration.
Code methods of image reconstruction from projections as used in  X-ray and PET Computed Tomography. Be able to explain  the nature of residual image artifacts and propose methods for their removal.
Implement simple methods of Magnetic Resonance Imaging from observed data.

Link to the syllabus on Studieportalen.

Study plan

Examination form

Most course points are allocated via a traditional written exam at the end of the course, 80% of course points are given via the written exam. In order to pass the course a project must be executed, a  report written and handed in. 20% of the total course points are based on the grade given to the project work. Instructions on the project work including the hand-in date for the project report are given under the Project Module.  The written exam will be held on the morning of 3rd June.

The final grade depends on this sum of points for the written exam (out of 20pts) and the project  (out of 5 pts). The Grade boundaries will be

                                     Total Points (exam + project)

           Grade 3              12.0 -  15.99     

            Grade 4              16.0 -  19.99

            Grade 5              20.0  – 25.0

 

The rules for aids during the exam are;

‘Calculators (of Chalmers approved type) are allowed. Laptops, tablets etc are not allowed. Mobile phones cannot be used during the exam. One page of handwritten notes (both sides can be used) is allowed. A English-Swedish Dictionary can be brought into the exam. No other written or printed material can be brought into the exam. Questions can be answered in English or Swedish but English is preferred. John and Kirsten try to visit the exam room twice to resolve any queries about the questions, once about 90 minutes after tthe start and then 3 hours after the start.'

 

Course summary:

Date Details Due