Bienvenue aux étudiants des groupes de TD1 et TD2 de la division A

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)