Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
Swedish title | Introduktion till bildbehandling med MATLAB: med fokus på neurovetenskap |
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English title | Introduction to Image Processing using MATLAB: with a Focus on Neuroscience |
Course number | 5685 |
Credits | 1.5 |
Responsible KI department | Institutionen för klinisk neurovetenskap |
Specific entry requirements | |
Grading | Passed /Not passed |
Established by | The Committee for Doctoral Education |
Established | 2023-03-15 |
Purpose of the course | This course introduces the basics of image processing concepts with a particular emphasis on using MATLAB to perform practical image processing methods in neuroimaging as well as biological/medical applications. This includes basic techniques for data extraction, histogram/thresholding/morphological operations, noise removal, image quality enhancement, filtering, segmentation, and registration. The contents of this course (both theoretical concepts and MATLAB codes/functions) will be useful for various image-processing applications from the microscopic workflow (e.g., cell counting, detection, labeling, classification, and tissue segmentation) to animal/human brain image analyses (e.g., structural and functional images collected by CT, PET, and MRI). |
Intended learning outcomes | At the end of the course, the students are supposed to be able to:
• explain the main theoretical concepts behind image processing methods • implement MATLAB for image-processing of their own data • understand software/packages designed for neuroimage processing/analysis, such as FSL and SPM in neuroimaging |
Contents of the course | Image Representation: read the matrix of data, understand the concepts of image pixel/voxel, image resolution and dimension, visualize 2D and 3D images, save the matrix of data
Image operations: count, find min and max, perform add/subtract/divide/multiply, binarize an image, create a mask Image histogram: understand the concepts of image intensity, colormap, and intensity/color distribution, and change the contrast of the image Image size and dimension: resampling and cropping Edge detection, Object labeling, Object dilation, and erosion Image Filtering: noise removal, smoothing Image Segmentation: segment/parcellate the image objects Image Registration: align images from different subjects or from different modalities, overlay images, perform mask-based image operations |
Teaching and learning activities | The theoretical content of the course will be taught in a form of lectures and pre-recorded videos with subsequent discussions using flip-the-classroom teaching methods. Besides the theoretical content, a series of MATLAB-based examples will show students how to implement image processing techniques in MATLAB. Teaching sessions will be complemented by hands-on sessions to help students practice their programming skills in MATLAB. Finally, there will be some hands-on projects to test how well students can apply the image processing methods with MATLAB. |
Compulsory elements | Attending lectures and hands-on sessions is mandatory. Absence from a lecture or session may be compensated by doing the hands-on for the corresponding topic. Reporting the codes of hands-on projects is mandatory. The examination is compulsory. |
Examination | The examination will be based on the assigned hands-on projects. In the last session, the theoretical explanation of the assignment as well as the results of the project (performed on MATLAB) will be presented in front of the other students. |
Literature and other teaching material | Recommended resources:
Digital Image Processing Using MATLAB, 3rd Ed. Gonzalez, Woods, and Eddins, 2020 McAndrew, Alasdair. "An Introduction to Digital Image Processing with Matlab Notes for SCM2511 Image Processing 1 Semester 1, 2004." https://mathworks.com/products/image.html |
Course responsible |
Fahimeh Darki Institutionen för klinisk neurovetenskap fahimeh.darki@ki.se |
Contact person |
Fahimeh Darki Institutionen för klinisk neurovetenskap fahimeh.darki@ki.se |