The course syllabus contains changes
See changesCourse syllabus adopted 2023-02-16 by Head of Programme (or corresponding).
Overview
- Swedish nameMedicinsk bildbehandling
- CodeSEE120
- Credits7.5 Credits
- OwnerTKMED
- Education cycleFirst-cycle
- Main field of studyBiomedical engineering
- DepartmentSPACE, EARTH AND ENVIRONMENT
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 73111
- Maximum participants80
- Open for exchange studentsNo
- Only students with the course round in the programme overview.
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0121 Project 1.5 c Grading: UG | 1.5 c | ||||||
| 0221 Examination 6 c Grading: TH | 6 c |
|
In programmes
Examiner
Kirsten Knudsen- Deputy Head Of Department, Space, Earth and Environment
Eligibility
General entry requirements for bachelor's level (first cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
The same as for the programme that owns the course.Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Aim
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.Learning outcomes (after completion of the course the student should be able to)
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).
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 about multiscale analysis and specifically about wavelets. Furthermore, be able to explain the applications of wavelets to de-noise and/or compress images.
List common image formats used in medicine. Be able to justify the use of lossless versus lossy compression for different applications.
Describe the basics of geometric transformation of images and methods of image registration and be able to implement simple methods of this by writing simple software codes.
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.
Content
- Brief introduction to medical imaging modalities (Computed Tomography (CT) X-ray, Positron Emiision Tomography (PET), Magnetic Resonance Imaging (MRI), ultrasound, optical imaging).- Image Enhancement: transform functions and histogram equalisation; image smoothing and sharpening in 2D and 3D; edge detection and noise reduction.
- Fourier domain methods of image enhancement and the implementation of such methods via 2D Discrete Fourier Transforms.
- Basic introduction to multiscale analysis, wavelets and and their applications to image de-noising and compression.
- The DICOM medical Image format.
- Image Registration methods and methods of geometrical transformation.
- The difference between lossy and lossless compression. Lossless Image Compression as implemented by Huffman coding and run length coding. Lossy image compression as implemented by JPEG compression.
- General methods of image restoration including inverse,pseudoinverse and Wiener filtering.
- Image reconstruction from projections including filtered back-projection.
Organisation
Lectures, lab exercises, problem classes and project.Literature
Digital Image Processing - 4th Edition - Global edition- by Gonzalez and Woods.Examination including compulsory elements
Compulsory Project and graded written exam.The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.
The course syllabus contains changes
- Changes to course rounds:
- 2023-11-10: Examinator Examinator changed from John Conway (jconway) to Kirsten Knudsen (kraiberg) by Viceprefekt
[Course round 1]
- 2023-11-10: Examinator Examinator changed from John Conway (jconway) to Kirsten Knudsen (kraiberg) by Viceprefekt