08-26, 16:00–17:30 (Europe/Berlin), Room 6
Scientists are producing more and more images with telescopes, microscopes, MRI scanners, etc. They need automatable tools to measure what they've imaged and help them turn these images into knowledge. This tutorial covers the fundamentals of algorithmic image analysis, starting with how to think of images as NumPy arrays, moving on to basic image filtering, and finishing with a complete workflow: segmenting a 3D image into regions and making measurements on those regions.
This tutorial is aimed at folks who have some experience in scientific computing with Python, but are new to image analysis. We will introduce the fundamentals of working with images in scientific Python. At every step, we will visualize and understand our work using Matplotlib. The tutorial will be split into three parts, of about 30 minutes each:
- Images are just NumPy arrays. In this section we will cover the basics: how to think of images not as things we can see but numbers we can analyze.
- Changing the structure of images with image filtering. In this section we will define filtering, a fundamental operation on signals (1D), images (2D), and higher-dimensional images (3D+). We will use filtering to find various structures in images, such as blobs and edges.
- Finding regions in images and measuring their properties. In this section we will define image segmentation — splitting up images into regions. We will show how segmentation is commonly represented in the scientific Python ecosystem, some basic and advanced methods to do it, and use it to make object measurements.
Start analyzing images with scikit-image, the numpy-native image processing library of the scientific python ecosystem!
Category [Data Science and Visualization] –Image Processing
Expected audience expertise: Domain –none
Expected audience expertise: Python –some
Public link to supporting material –https://scikit-image.org/euroscipy24-tutorial/lab/index.html?path=00_start.ipynb
Lars is currently working as a freelance and core developer for the image processing library scikit-image. With an education in electrical engineering and a focus in health and sensor technologies, he has worked as a research assistant on adaptive ultrasound imaging at the TU Dresden. As a student, he started contributing to the scientific Python ecosystem and discovered his interest for signal processing, Linux, and especially Python’s scientific ecosystem.
Marianne Corvellec is a core developer of scikit-image, a popular Python library for scientific image processing, where she specializes in biomedical applications. Her technical interests include data science workflows, data visualization, and best practices from testing to documenting. She holds a PhD in statistical physics from École normale supérieure de Lyon, France. Since 2013, she has been a regular speaker and contributor in the Python, Carpentries, and FLOSS communities.
I work at the intersection of computation and research, with a focus on improving open source tooling and supporting the community of developers. I've been involved with scientific Python since the early 2000s, and founded scikit-image in 2009. I am a co-author of Elegant SciPy, a community leader in the Scientific Python project (https://scientific-python.org), and thoroughly enjoy my collaborations with members of this community. I am originally from South Africa, and was privileged to attend my first EuroSciPy (which I loved!) in 2011.