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FSK3522 Quantitative Data Analysis and Processing for Microscopy 7.5 credits

Advanced microscopy methods, such as confocal and super-resolution microscopy, generate large amounts of data. These data can often be represented as images and analyzed by different methods.
This course has an emphasis on methods for developing application-specific solutions to extract quantitative data from image information with Matlab, lmageJ and similar tools.

Course offering missing for current semester as well as for previous and coming semesters
Headings with content from the Course syllabus FSK3522 (Autumn 2018–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

This course focuses on the mathematical basis and implementation of microscopy image data processing, data extraction, and data analysis. The course covers intensity and color-based transformations and segmentations, Fourier methods for both filtering and analysis and morphological operations. The student will be expected to be able to both analytically solve problems and to independently choose methods and implement them to solve a "real" task.

Intended learning outcomes

After completing the course, the student should be able to (with emphasis on image data from light microscopy)

  • Explain and use the mathematical basis of intensity transformations and spatial filtering in up to four dimensions.
  • Implement solutions based on this knowledge in Matlab, ImageJ, Imaris or similar computational toolkits, as well as use the built-in methods.
  • Explain and use the mathematical basis of frequency domain filtering (Fourier methods) in up to four dimensions as well as deconvolution.
  • Implement solutions based on this knowledge in the toolkit, as well as use the built-in functions
  • Take into account the effects of color space choice, perform mathematically valid color space transformations and color-based transformations and segmentations
  • Explain and use some basic mathematical algorithms for image compression
  • Explain and use basic and coumpound morphological operations and implement solutions based on built-in methods.
  • Explain and use the mathematical basis and methods of image segmentation.
  • Implement solutions based on this knowledge in the toolkit, as well as use the built-in functions
  • Know the advantages and challenges of working with different types of super­ resolution images (STORM, PALM, SIM, STED) and the mathematical foundations of the image (re)construction algorithms
  • Extract relevant data from processed images and perform mathematical analysis thereof, including nonlinear regression, simple optimization problems and fitting to partial differential equations
  • Build, motivate and document a GUI in Matlab, ImageJ or similar toolkit to handle a specific multi-step image processing and analysis task (project work)

Course disposition

The course is based on 12 seminars where the theoretical aspects are addressed in parallel with the literature. The practical part of the course consists of two exercises and a larger project. The project should be related to the student's own research and process microscopy measurement data.

Literature and preparations

Specific prerequisites

Admitted to PhD studies in Physics, Biological physics or related fields of study.

Recommended prerequisites

Basic knowledge of Matlab, ImageJ or similar tools.
Basic knowledge of theoretical and practical microscopy
English good enough to follow the course and participate in discussions


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RC Gonzalez & RE Woods, Digital Image Processing, 3rd ed (ISBN-13:978-0-13-505267-9)
Bioimage Data Analysis, edt Kota Miura, ePub ISBN: 978-3-527-80094-0
Handout "Analyzing fluorescence microscopy images with lmageJ", by Peter Bankhead

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale



  • INL1 - Assignment, 1.5 credits, grading scale: P, F
  • INL2 - Assignment, 1.5 credits, grading scale: P, F
  • PRO1 - Project work, 4.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Examination is by written assignments and a project. The project work is presented at a seminar.

Other requirements for final grade

Completed the following: Hand-in assignments 1, Hand-in assignments 2 and the Project work

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Profile picture Hjalmar Brismar

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FSK3522

Offered by

SCI/Applied Physics

Main field of study

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Education cycle

Third cycle

Add-on studies

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Hjalmar Brismar (

Postgraduate course

Postgraduate courses at SCI/Applied Physics