Online courses

These interactive, web-based courses include theoretical background, practical guides, and quizzes that provide the groundwork for your own image analysis.

Beginner ImageJ

Learning Objectives

• Understand the differences between ImageJ, ImageJ2, and FIJI
• Successfully install and configure the software
• Configure memory settings across different platforms
• Navigate the basic user interface

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Intermediate ImageJ

Learning Objectives

• Apply advanced threshold methods (Triangle, Yen, Huang, etc.)
• Understand global vs. local thresholding
• Use morphological operations (Open, Close, Erode, Dilate) to refine masks
• Handle noisy or multi-channel images and overlapping objects
• Understand and apply basic watershed segmentation (e.g., common pitfalls)

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Advanced ImageJ

Learning Objectives

• Master the Macro Recorder to create and modify automation scripts
• Write custom functions and control structures in ImageJ macro language
• Implement batch processing workflows using loops and conditionals
• Debug and optimize macro performance
• Integrate user input and file handling in macros
• Convert macro workflows to Python scripts using PyImageJ

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Feature Engineering & Selection (Python)

Learning Objectives

• Define “feature” in the context of machine learning, including when to apply different transformations.
• Implement and compare common feature extraction methods (numeric, categorical, textual) in Python.
• Recognize the relationship between feature engineering, model performance, and best practices.
• Evaluate and compare extracted features using statistical measures while avoiding pitfalls like data leakage.

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