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This course will provide a comprehensive understanding of the usage of the open-source software ImageJ for
scientific image analysis. The course will cover everything from basic manipulations to custom scripting. Students will learn how to create functional workflows for the analysis. The course is suitable for total beginners as well as for intermediate users. The course will be held in hybrid mode for a maximum of 20 students in personal attendance and virtually unlimited students online. Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)
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ImageJ study https://imagej.nih.gov/ij/docs/examples/index.html Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)
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Examination based on project report. Last update: Sacherová Veronika, RNDr., Ph.D. (11.05.2023)
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Lesson 1: Image Formation in microscopy modalities, resolution and super-resolution, noise, image dimensions, file formats, workflows, image preprocessing, image restoration, image processing, segmentation, object classification, measurements, statistics, data visualization, data management, FIJI introduction. Lesson 1: Basic image handling in FIJI Lesson 2: Preparing images for analysis Lesson 3: Basic measurements and image segmentation Lesson 4: Machine learning techniques and advanced image processing and segmentation Lesson 5: CLIJ GPU accelerated analysis, FIJI customization Lesson 6: Introduction to macro language, scripting, and batch processing Lesson 7: Programming fundamentals, writing scripts in macro language Lesson 8: Writing custom scripts for batch analysis Lesson 9: Colocalization analysis Lesson 10: Advanced microscopy techniques analysis and image processing Lesson 11: Implementation FIJI in large workflows, using imageJ in computing cluster Lesson 12: Students projects presentation Last update: Sacherová Veronika, RNDr., Ph.D. (12.05.2023)
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