| Responsible Department | Department of Basic Science and Environment | ||||||||||||||
| Earliest Possible Year | MSc. 1 year to MSc. 2 year | ||||||||||||||
| Duration | One block | ||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||
| Level of Course | MSc Also functions as a PhD course. | ||||||||||||||
| Examination | Final Examination oral examination All aids allowed Description of Examination: Oral presentation of the project in presence of all course participants. Weight: Oral examination on the basis of project report 100%. 7-point scale, no second examiner | ||||||||||||||
| Requirement for Attending Exam | Presence at at least 75% of the exercises and turning in a project report. Projects may be done in groups. | ||||||||||||||
| Organisation of Teaching | The course is given every 2nd year, in even years. Lectures/consultation about 4 hours/week, Computer exercises/project work about 8 hours/week, oral presentations of projects the last day. Students must bring their own laptop computers for exercises. | ||||||||||||||
| Block Placement | Block 4 Week Structure: A, Only even years (2010, 2012, ...) | ||||||||||||||
| Language of Instruction | English | ||||||||||||||
| Optional Prerequisites | 210003 | ||||||||||||||
| Restrictions | None | ||||||||||||||
| Course Content | |||||||||||||||
| The following subjects will be covered: Digital image representation and -compression, filters, image improvement (low level image processing), simple characterisation of images (e.g. histograms), edge detection, texture, segmentation and counting of objects, morphological transformations, characterisation of objects in images, transformation to the frequency domain (Fourier transformation), analysis in the frequency domain, colour spaces and multispectral imagery, template matching, 2D image registration. Examples will be partly from research groups at KU/LIFE using image analysis; these groups are consulted during the development of the course. An open source / free standard tool (ImageJ) will be used in the course, and apart from acquiring basic skills using the tool the participants will also have to learn to make simple extensions and adaptations (through simple programming). In the last part of the course the participants will be working on projects. A project could for example focus on making an application which in a user friendly fashion solves a specific image analysis problem with a "known" algorithm (e.g. counting cells in a microscopy mage) or a project could be more "experimental" in nature and seek algorithms to extract information from a certain type of images (e.g. segment different kinds of tissue in ultrasound images). At the end an oral presentation of each project must be made for the other participants. The grade obtained for the course will be based on the oral presentation. | |||||||||||||||
| Teaching and learning Methods | |||||||||||||||
| In the first part of the course, days will have a mixture of lectures and hands-on computer exercises. In the second part of the course, during which the participants are working on projects, there will be scheduled time for consultation on the projects. | |||||||||||||||
| Learning Outcome | |||||||||||||||
| The objective of the course is to introduce the participants to automatic image analysis and digital images, mainly with biological applications. The participants shall learn about fundamental problems in image analysis and methods to solve them. They shall furthermore learn to use and through simple programming extend and adapt a standard program for image analysis. After completing the course the student should be able to: Knowledge: -Describe the effect/behaviour of key image analysis methods, including the meaning of their parameters. -Explain on a general level the theory behind key image analysis methods. -Reflect about the appropriateness of an algorithm proposed for a given image analysis problem. -Display knowledge of basic principles of programming on the script level. Skills: -Select and apply low level image processing methods to achieve a desired effect. -Implement a simple image analysis algorithm through scripting of a standard software tool. Competences: -Design and develop an image analysis algorithm to solve an image analysis problem through the combination of standard image analysis tools. -Communicate to peers the principles of an image analysis algorithm developed. -Communicate to end users the scope, use and limitations of an image analysis solution developed. | |||||||||||||||
| Course Literature | |||||||||||||||
| John C. Russ: The Image Processing Handbook (5th ed., 2006). CRC Press. ISBN: 0849372542. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Morten Larsen, ml@life.ku.dk, Department of Basic Sciences and Environment/Mathematics & Computer Science, Phone: 353-32390 | |||||||||||||||
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