Curricula Guides
TÄHT7041 Signal and image processing 4 ECTS
Organised by
Astronomy
Person in charge
Seppo Mattila
Preceding studies
Compulsory:

Learning outcomes

After completing the course the students should be able to:
(1) Describe the principles behind some advanced astronomical imaging techniques and identify suitable topics in astrophysics that can be studies with them;
(2) Understand the physics behind some of the most important medical imaging modalities and describe their value in clinical applications;
(3) Identify and discuss the differences and similarities in the challenges faced when analyzing data in these two different disciplines;
(4) Describe the theoretical basis and suitability of several image/signal processing and analysis methods commonly used in astronomy and medical imaging;
(5) Identify suitable algorithms and apply them to astronomical and/or medical imaging datasets to enhance their scientific and/or clinical value;
(6) Produce a written course report

Contents

The course is offered for MSc students of astronomy and space physics, medical physics and materials research. The course includes teaching in the form of lectures and supervised hands-on work on astronomical and medical imaging datasets. The lectures introduce the students to the physics behind some of the state of the art medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) and their clinical diagnostic value. Advanced astronomical imaging methods such as adaptive optics (AO) imaging and interferometric imaging techniques are introduced with examples from recent research. During the practical sessions the students will work on real astronomical and medical imaging data learning about the commonly used image/signal processing and analysis methods as implemented in Python (or Matlab). This can include e.g. application of methods for filtering, smoothing and convolution, image reconstruction and deconvolution, PSF matching and image subtraction, fourier transform, automatic detection and characterization of objects, segmentation, registration and data fusion. The students are expected to work individually on their datasets and write-up their individual course reports at the end of the course.

Teaching methods

Teaching method Contact Online
Lectures 18 h 0 h
Independent work
Exercises 24 h 0 h

Teaching language

English

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Doctoral Students
  • Exchange Students
Participation in classroom work
  • In English
Exercise(s)
  • In English

100% participation in the lectures and practical sessions, course report

Evaluation

Numeric 0-5.

Further information

Period V, 2018

Belongs to following study modules

Department of Physics and Astronomy
Department of Physics and Astronomy
Department of Physics and Astronomy
Department of Physics and Astronomy
Department of Chemistry
2016–2017
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Implementation details are unavailable.
Department of Physics and Astronomy
Degree Programme in Physical Sciences
DP in Physics Education Track
Degree Programme in Physical Sciences
DP in Theoretical Physics
Finnish Study Modules