I. The image data collection and image pre-processing
Essentials (pixel, voxel, resolution, levels of grey, matrix); Luminescence, intensity and color; Image as a data matrix; Human eye parameters in comparison to digital image parameters; Formats of image files (binary, greyscale, RGB, HSV, Lab); Basic filtration and image pre-processing; Look-up table, histogram, contrast and brightness, gamma correction.
Basic and advanced segmentation methods, including artificial intelligence (AI) approaches; Image deconvolution; Introduction into IJM (ImageJ Macro Language) and macro development.
Filtration and image preprocessing in 2D/3D using linear or morphological filters; Measurement and counting of segmented objects in 2D/3D; Area/volume measurement by pixel counting; Perimeter, length and surface area measurement using Crofton formula on binary images; Construction of geometric models from 3D data; Length and surface area measurement using 3D models.
II. Biological applications of the image analyses
Evaluation of co-localization of light microscopy images; Particle tracking; FRAP data analyses; Colocalization and clustering in electron microscopy data; Visualization and measurement of capillaries in 3D confocal data.
Introduction to stereology; Sampling in stereology; Cavalieri’s principle for measuring volume; Point-counting method; Methods for measuring length and surface area from thin sections; Methods based on focusing through thick sections: disector principle for counting three-dimensional particles (e.g., cells), methods for length measurement of spatial curves (e.g., capillaries) and surface area.