Python Workshop: Image Processing

Description

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.

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: Karin Sasaki (CBM), Jonas Hartman (Gilmour Lab), Kota Miura (ALMCF), Toby Hodges (Bio-IT)

Bookings