Image Processing with Python

The workshop will take place at Science Park 402, 1098 XH Amsterdam. Please note that lunch and drinks at the end of the workshop are included.

This is a Data Carpentry workshop that will teach you to about digital images and how to analyze them using code. The workshop will cover the basics of digital images, manipulation with Python and the scikit-image (skimage) library. Both deeper image understanding and automation of image processing tasks will be covered.

By the end of the workshop you should be able to enhance your research with images be they from microscopy, radiology or another discipline.

The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the workshop website (will be activated once registration is live, usually 3 weeks before the start of the workshop).

Cancellation and No-Show Policy

Please be advised that by signing up, you agree to our Cancellation and No-Show Policy, which states that cancellations made less than 2 workings days prior to the event will incur a no-show fee. Please read the full policy here for more details.

If you won’t be able to attend, please cancel your registration (by following the instructions here) so that we can offer your seat to someone on the waiting list.

Audience

The workshop is aimed at PhD candidates and other researchers or research software engineers. We offer tickets for researchers who are affiliated with Dutch research institutions. We also offer industry tickets for attendees who are not affiliated with Dutch research institutions. We do not accept registrations by Master students.

Ticket prices

Ticket prices are as follows:

• For participants affiliated with Dutch research institutions: €225.00
• For participants from industry: €675.00

Prerequisites

The participants should:

• Be familiar with Python (you should be able to write simple functions)
• Be interested in analysis of images towards scientific goals

Syllabus

• Introduction to images : Digital Image Basics
• Working with skimage: How can the skimage Python computer vision library be used to work with images?
• Drawing and Bitwise Operations
• Creating Histograms: How can we create grayscale and color histograms to understand the distribution of values in an image?
• Blurring Images: Intro to filtering and blurring
• Thresholding: How can we use thresholding to produce a binary image?
• Connected Component Analysis: How to extract separate objects from an image and describe these objects quantitatively.
• Capstone Challenge: solving a real world microscopy problem
• Bonus session: Preparing images for machine learning (affines, other transformations and more)