Navigation auf uzh.ch
Navigation auf uzh.ch
To help you get started with image processing and data analysis, we’ve provided some basic, easy-to-follow tutorials. Originally created for lectures and courses, we are now sharing them here due to numerous requests from users of our facility.
In addition, be sure to also check out our guides section for additional step by step tutorials.
In the first session of the Bioimage Analysis Lunch Seminar series, we introduced the fundamental concepts of image processing and analysis using Fiji/ImageJ. Due to your many requests and interest, we are also sharing the seminar materials here.
You'll find the seminar outline, along with additional handouts that provide step-by-step guidance on image visualization, basic segmentation, and analysis workflows.
ImageJ/Fiji is one of the most popular free, open source tool for image processing and data extraction at our facility. It is simple, powerful, extendable by plugins and supports scripting languages for an easy automation of image processing tasks.
Session 1 - Fiji
LunchSeminar_2024_FiJi_Session1_JRM (PDF, 5 MB)
Intro Fiji Image processing 2024 (PDF, 3 MB)
Intro Fiji image processing demo data 2024 (ZIP, 17 MB)
Session 2 - Fiji automation
LunchSeminar Session2 Presentation (PDF, 1 MB)
LunchSeminar Session2 Macros and data (ZIP, 2 MB)
LunchSeminar Session2 Tutorial (ZIP, 73 MB)
Session 3 - Ilastik
LunchSeminar BiovisionCenter Education
Session 4 - Cellpose
LunchSeminar BiovisionCenter Education
Session 5 - Imaris
Bitplane Imaris is a commercial software package for the visualization, segmentation and analysis of multidimensional microscopy datasets. Powerful 3D visualizations, a simple user interface together with wizard-based segmentation allows to achieve relevant results without extensive knowledge in image processing.
Ilastik is a simple, user-friendly free open source tool for interactive image classification, segmentation and analysis. At the ZMB, we use it as a segmentation and particle tracking solution for projects where classical intensity based thresholding strategies fail. Ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, Ilastik infers a problem specific segmentation.
