Acquisition of skills that enable the student to
correctly visualize and interpret biological images and obtain quantitative
data. Automation of tasks using the ImageJ macro language.
1/ Describe and explain the theoretical bases of digital image interpretation:
digital image, image formats, spatial sampling, convolution and deconvolution
functions, periodic and non-periodic signals, filters, quantification
(segmentation, grain size densitometry, contrast calculation, signal-to-noise
ratio determination, image correlation theory, statistical approaches for
co-location analysis, Fourier analysis).
2/ Explain the basic principles of 3D reconstruction: image combination, back
projection methods, iterative methods.
3/ Use ImageJ (Fiji) to perform channels splitting and merging, segmentation by
thresholding, densitometry, granulometry, quantification on protein/DNA gels
and microscopy images, deconvolution, intracellular co-localization, filters,
periodic signals, projection simulation, 3D reconstruction on projected data,
macro language programming with user interface, 3D rendering.
4/ Master scientific argumentation (evidence against points of view: reasoning,
facts, examples).
no prerequisites
Digital Image Processing, W. Burger and M.J. Burge, Springer (2nd Edition)
This course will be running for one-week full time. Each new concept will be
immediately put into practice on concrete examples using computers and ImageJ /
Fiji software. Active learning methodology (Jean Piaget, John Dewey and Kurt
Lewin) by combining maieutic educational methods, and debate (Oscar Brenifier)
and spiral learning (J.C. Bruner)
- Enseignant: Frédéric Coquelle
- Enseignant: Sophie Dupre
- Enseignant: Karina Goncalves Gaspar