Date(s) - 2016-04-06 - 2016-04-07
09:30 CEST - 12:00 CEST
This is a tutorial to exemplify fundamental concepts of automated image processing and segmentation, using Python.
This course assumes a basic knowledge of the Python Programming Language. For those at EMBL, this means that you have participated in a beginners course for programming, preferably a Python course.
Segmentation of 2D confocal fluorescence microscopy images of a membrane marker in confluent epithel-like cells.
Programming Concepts And Content Discussed In This Tutorial
- Python scripts, functions
- Standard variable types: array, dictionaries
- Control flow
- Modules, packages, importing modules and packages and using them
- Importing data
- Using the documentation
- Arrays and manipulation (dimensions, indexing, slicing, arithmetic)
- Visualising images
- Debugging by printing relevant information and plotting images at appropriate stages
- Exporting data and writing files
Image Processing Concepts And Content Discussed In This Tutorial
- Loading and visualising images
- Images are arrays of numbers; they can be indexed, sliced, etc…
- Images contain 3 types of information: Intensity, Shape, Size (a good segmentation pipeline uses them all)
- Preprocessing: smoothing, background substraction
- Segmentation: adaptive thresholding, distance transformation, detection of maxima, watershed
- Filtering: Discarding undesired objects, e.g. fused cells
- Analysis: Extracting measurements from segmentation
- Saving output (segmentation, data, graphs)
- Automation (for all files in a directory): demonstration of advantages and example applications of automated image analysis
Tutors: Karin Sasaki (CBM), Jonas Hartman (Gilmour Lab), Kota Miura (ALMCF), Toby Hodges (Bio-IT)
This event is fully booked.