biovoxxel
Basic Microscopic Image Processing and Analysis
Introduction
Introduction
Course Preparations
Fiji Installation and Configuration
Exercise Image Download and General Info
Introductions to the Fiji User Interface (GUI)
Basic Fiji Functions Important to Know
Before You Start - Important Core Concepts!
Proper File Formats for Scientific Image Data
Behind the Scenes - Image Metadata
Channel Merging & Proper 2D and 3D Image Scaling
The Image Histogram - More than just a Plot
Where is the Limit? - Why Bit-Depth Matters
Grayscale or Color?
Let's directly jump in
Simple Object Counting
A Word on Manual Image Analysis
Introduction to Image Segmentation
What is Image Segmentation & Why do we need it?
Segmentation Challenges and Definitions: Background ≠ Noise ≠ Unspecific Signal ≠ Uneven Illumination
Pre-Processing
The Image Processing Workflow or Why Sequence Matters!
Pre-Processing - Illumination Correction
Considerations on Image Illumination Issues
Quantitative Illumination Correction (for Measurements)
Artificial Illumination Correction (for Segmentation)
Pre-Processing - Image Filtering
Why Understanding Image Filters is Important!
Exploring Image Filters Practically
The Image Filter Compendium
Pre-Processing - Unspecific Signal Reduction
Get it Clean - Unspecific Signal Reduction
Pre-Processing - Contrast Enhancement
Why Pixel Intensity Normalization is Important in Image Segmentation
Local Contrast Enhancement
Segmentation - Thresholding
Proper Thresholding & Quality Control
Assignment - Image Segmentation
Assignment Description
Assignment Solution
Machine Learning - Pixel Classification as Alternative to Thresholding
Trainable Weka Segmentation
LabKit - Pixel Classification for Very Big Images and Volumes
Post-Processing
Object Separation
Morphological Post-Processing
Image Analysis and Measurements
Important Considerations BEFORE Performing Measurements
Counting Objects and Measure Size, Shape and Intensity
Extended Object Analysis
Analyzing Secondary Objects inside Primary Objects
Object-based "Co-Localization" (Area Overlap)
Line-like Structures, Length and Branching
Macro Recording and Automation
Automation - Batch Processing for Beginners
Color Image Segmentation
Color Spaces - How to Efficiently Separate Colors
Extracting ROIs from Color Images using Color Spaces
Extracting ROIs from Color Images using Color Excess
Overcoming Limitations of Pixel Classification with Classical Segmentation
Bonus 01 - 3D Image Segmentation
Why Z-Projections should NOT be used to analyze 3D Data
Adding one Dimension - Considerations for Segmentation of Volumetric Data
Voronoi Threshold Labeler - Single-Tool-Segmentation Workflow on the GPU
Bonus 02 - Analyzing Time Series (Tracking)
Automatic Tracking in Fiji (including Drift Correction)
Bonus 03 - Application: Practical Analysis Examples
Segmentation Examples (including Automation)
Basic Image Analysis
Basic Image Analysis
Why Z-Projections should NOT be used to analyze 3D Data
Intensity Z-projections are very often the go-to method for many scientists with 3D images, since the additional dimension makes the analysis more complicated and many struggle with an analysis in 3 dimensions. However, Z-projections are mostly not a good idea, because they neglect the third dimension and therefore often lead to inaccurate of wrong measurements. This is especially true for pixel intensity measurements.
Here, you see an explanation, why this is the case!
Here, you see an explanation, why this is the case!
