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Title Introduction to Image Processing using MATLAB: with a Focus on Neuroscience
Course number 5685
Programme Neurovetenskap
Language English
Credits 1.5
Date 2023-08-30 -- 2023-09-27
Responsible KI department Institutionen för klinisk neurovetenskap
Specific entry requirements
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
Number of students 8 - 16
Selection of students Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date)
More information The course will take place once a week, on Wednesday from 9:00 to 16:30, in Campus Solna. Exact rooms will be announced later.
Additional course leader
Latest course evaluation Not available
Course responsible Fahimeh Darki
Institutionen för klinisk neurovetenskap

fahimeh.darki@ki.se
Contact person Fahimeh Darki
Institutionen för klinisk neurovetenskap

fahimeh.darki@ki.se