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Course Catalog 2013-2014
SGN-55006 Introduction to Medical Image Processing, 5 cr |
Person responsible
Ulla Ruotsalainen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
To pass the course the student has to make all the computer exercises, pass the exam and make a small assignment.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
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Learning Outcomes
The objective of the course is that the student will have skills and knowledge on tomography imaging in medicine. The students will have basic knowledge on multimodality image registration and segmentation methods.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Basic principles of the image reconstruction methods in tomography imaging. This includes Radon tranformation, BP, FBP and MLEM. | OSEM and noise penalization (one step late and MRP) | Mathematical proofs of the reconstruction methods. |
| 2. | Image coregistration for medical use, problems and image fusion techniques. | ||
| 3. | Structural imaging versus functional imaging in the view of automatic analysis of the image sets. | ||
| 4. | Archiving of biological and medical images: technical, ethical and legal aspects. |
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Medical Image Processing, Reconstruction and Restoration | Jiri Jan | 0-8247-5849-8 | No | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-11000 Basic course in Signal Processing | Mandatory | 1 |
| SGN-11006 Basic Course in Signal Processing | Mandatory | 1 |
| SGN-12000 Basic Course in Image and Video Processing | Mandatory | 2 |
| SGN-12006 Basic Course in Image and Video Processing | Mandatory | 2 |
| SGN-13000 Introduction to Pattern Recognition and Machine Learning | Advisable | 3 |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | Advisable | 3 |
| SGN-01251 Signal Processing Applications | Advisable |
1 . SGN-11000 or SGN-11006 are advisable. Alternatively, equivalent knowledge.
2 . SGN-12000 or SGN-12006 are advisable. Alternatively, equivalent knowledge.
3 . SGN-13000 or SGN-13006 are advisable. Alternatively, equivalent knowledge.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |