• Recall digital image formation principles
• Understand human perception and color
• Distinguish between bit-depths
• Use lookup tables
• Perform mathematical operations on images
• Apply filtering to digital images
• Understand and use image segmentation techniques
• Create regions of interest and extract results from segmented images
• How to perform projections and reslicing on images for analysis
• Applying color deconvolution to brightfield images
• Understand the concepts of the ImageJ Macro language

Week 1: Digital Images
Introduction to digital image formation and how optical systems go from objects to images.

Week 2: Colors
Review of human visual perception and the RGB color model. Introduction to the concepts of image bit-depth and lookup tables.

Week3: Operating on Images
Introduction to image scaling, interpolation, and mathematical operations of images, and why certain bit-depths are more suitable than others.

Week4: Filtering
Using image filtering to enhance or suppress features in an image for easing subsequent analysis. We cover linear, nonlinear and Fourier filtering with emphasis on examples.

Week 5: Image Segmentation
Introduction to image segmentation and overview of available methods (thresholding, clustering, machine learning) and morphological operations.

Week 6: Regions of Interest
Going from analyzed objects to regions of interest and results tables. Emphasis is made on how to best obtain unbiased measurements and produce a reusable image analysis workflows

Week 7: Colors, and dimensionality reduction
Introduction to color models, and color deconvolution. Overview of the concept of dimensionality reduction through image projections and reslicing and application to measuring moving objects.

Extra Week: ImageJ Macro Programming Prime
Presentation of basic programming principles applied to the ImageJ Macro Language. Crash course on variables arrays, loops, conditionals, available macro functions and writing custom functions.