{"id":null,"code":"FFYS7110","name":{"valueFi":"Signal and Image Processing","valueEn":"Signal and Image Processing","valueSv":"Signal and Image Processing"},"credits":5.0,"minCredits":2,"maxCredits":5,"tags":[],"createdAt":1790534785371,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"","valueEn":"After completing the course the students should be able to:\r\n(1) Describe the principles behind some advanced astronomical imaging techniques and identify suitable topics in astrophysics that can be studies with them;\r\n(2) Understand the physics behind some of the most important medical imaging modalities and describe their value in clinical applications;\r\n(3) Identify and discuss the differences and similarities in the challenges faced when analyzing data in these two different disciplines;\r\n(4) Describe the theoretical basis and suitability of several image/signal processing and analysis methods commonly used in astronomy and medical imaging;\r\n(5) Identify suitable algorithms and apply them to astronomical and/or medical imaging datasets to enhance their scientific and/or clinical value;\r\n(6) Produce a written course report","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"","valueEn":"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 radio 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 e.g. 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.","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"","valueEn":"100% participation in the lectures and practical sessions, course report.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"","valueEn":"16h Lectures + 16h Exercises + independent work","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"Course will be given every other spring (in even years).","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"","valueEn":"The course requires the student to be capable of independent work to be able to complete the practical exercises.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"Hyväksytty/Hylätty","valueEn":"Pass/Fail","valueSv":"Pass/Fail"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Fysiikka","valueEn":"Physics","valueSv":"Physics"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Seppo Mattila, Kaj Wiik, Riku Klén, Jani Saunavaara, Jarmo Teuho","valueEn":"Seppo Mattila, Kaj Wiik, Riku Klén, Jani Saunavaara, Jarmo Teuho","valueSv":"Seppo Mattila, Kaj Wiik, Riku Klén, Jani Saunavaara, Jarmo Teuho"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}