This tutorial presents how to investigate an image, by extracting quantitative information. This tutorial is presented as an interactive study which is performed live, using Icy. The audience will participate and will propose interpretation of the problem, and of course, there will be a lot of traps! It covers a large number of topics: understanding the nature of noise in the image, understanding the interest of different representations of the images: the richness of 2D and 3D rendering in different modalities, the use of color maps and the practical use of histograms. In a second step, more advanced algorithms such as wavelets for spot detection and MHT for tracking of particles are also covered.
Students coming to this session will learn in a didactic and ludic way why the noise is so important in the images and what the good practices of the image analysis are. In this interactive session, they will discover that one needs to deeply understand his/her data before performing an analysis. Each step performed during this tutorial is reproducible since the software and the data are free and available for download. At the end of the session, the attendants can perform and extend their analysis directly on their laptops!
Course Details or Outline:
- Installing Icy
- Opening an image
- Understanding the histogram and the look up table (color map)
- Finding functionalities in seconds without knowing the software!
- The ImageJ compatibility.
- Website presentation, browsing script resources and graphical protocols.
- The screen is lying to us! Representation of 16bits images.
- Interpreting an atomic force microscopy output as an image.
- Viewing background information
- Viewing data that cannot be displayed in 2D with false color map.
- Characterization of noise
- Using a 3D ray-traced volume rendering vs a 3D elevation map rendering
- Interpretation of the histogram
- Data are lying to us: understand the defects of the data acquisition
- Detecting density of spots representing neurons in an image
- Automatically perform segmentation of cell in an image, and then density detection
- Interpreting results
- Interpreting results of several users over the same sample
- Detection of vesicle and tracking
- Description of global and individual movements
- Concluding remarks