Syllabus for |
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TMS016 - Statistical image analysis
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| Syllabus adopted 2012-02-22 by Head of Programme (or corresponding) |
| Owner: MPENM |
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7,5 Credits |
| Grading: TH - Five, Four, Three, Fail |
| Education cycle: Second-cycle |
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Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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Teaching language: English
Open for exchange students
| Course module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0101 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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30 May 2018 am SB
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Contact examiner
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29 Aug 2018 pm SB
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In programs
MPAPP APPLIED PHYSICS, MSC PROGR, Year 1 (elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)
Examiner:
Universitetslektor
David Bolin
Go to Course Homepage
Eligibility:
In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
Course specific prerequisites
The student is supposed to have completed a basic course in mathematical statistics worth of 7.5 credit points.
Aim
The aim of the course is to provide a basic knowledge of how to use probabilistic and statistical methods for image analysis.
Learning outcomes (after completion of the course the student should be able to)
- have experience with image acquisition and basic image processing,
- be able to understand how to use statistical image analysis
techniques including methods for pattern recognition
- have competence of applying statistical modeling to image analysis, including the use of models for point patterns and spatial correlation.
Content
Digital image processing and analysis of information in images are methods that become increasingly important in many technical and scientific fields, including almost all biological sciences.
Methods for acquiring, showing, filtering and segmentation of images are briefly covered in the first part of the course, including methods for performing quantitative measurements in images.
Core subjects in the course are pattern recognition and spatial
statistics applied to images. In pattern recognition we study methods for discrimination between classes of objects characterized by suitably chosen features. Spatial statistical models are used for describing point patterns, spatial correlation and the shape and structure of objects in two and three dimensions.
Examples are taken from remote sensing, microscopy, photography, medical imaging and fingerprint analysis. In the course special interest will be devoted to applications in bioinformatics, including analysis of images of microarrays for comparing DNA expression levels and images of two-dimensional electrophoresis gels for studying proteins.
Organisation
Lectures.
Practical computer work is included, typically using MATLAB. An
important part of the course is to carry through a project in a small
group, presenting the results at a seminar and writing a project
report.
Literature
To be decided
Examination
The assessment is based both on a written final examination and the project report.