Image Processing with Python

Description

This is a tutorial to exemplify fundamental concepts of automated image processing and segmentation, using Python.

This course assumes some 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.

• Day 1 (13:00-17:00): Handling numerical data and exploratory data analysis in Python
• Day 2 & 3 (09:30-17:00): Automated processing of biological images in Python

Task

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: Jonas Hartmann (Gilmour Lab), Toby Hodges (Bio-IT/Zeller Team)

Bookings