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Syllabus for

Academic year
TMS016 - Statistical image analysis
 
Syllabus adopted 2012-02-22 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Open for exchange students
Block schedule: X

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5c Grading: TH   7,5c   28 May 2014 am V,  21 Aug 2014 am V

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)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)

Examiner:

Professor  Mats Rudemo



  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

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.


Published: Mon 28 Nov 2016.