260004 VU Scientific image processing (2024W)

OBJECTIVES
This course aims to equip students with fundamental knowledge and skills in quantitative image processing and analysis for scientific applications. It will enable students to independently develop, execute, and optimize basic image analysis workflows. The curriculum focuses on extracting meaningful data from images without compromising their integrity.

SYLLABUS
#### Foundational Concepts
- Digital Imaging Basics: Bit depth, color, and lookup tables
- Regions of Interest (ROIs)
- Quantitative Intensity Analysis
- Algebraic Operations with Images

#### Advanced Techniques
- Image Correlations, Convolutions, and Deconvolutions
- Fourier and Other Image Transforms
- Image Filtering Techniques: Linear, Non-linear, and Fourier-based
- Image Thresholding and Segmentation
- Annotation and Labeling of Images

### Instructional Approach
The course adopts an interactive pedagogical model that involves student participation and ongoing assessment through individual and group assignments. While the course is centered around the open-source image analysis software, ImageJ/Fiji, students are encouraged to adapt the workflows to other software or programming languages like Matlab, Labview, C++, Python, Julia, or R.

### Topic Breakdown (Tentative)
1. **Course Introduction**: Image significance; image definitions; scope and limitations of image processing; digital image parameters; image sampling and quantization.

2. **Image Basics**:
a. Histograms, Lookup Tables, Color Formats, Human Visual System, Pseudocolors, and Image Types.
b. Overview of ImageJ/Fiji: Native formats, plugins, properties, and user interface.

3. **ROI and Segmentation**: Managing ROIs, thresholding techniques, and morphological operators in ImageJ/Fiji.

4. **Image Filtering and Macro Operations**: Understanding images as matrices; performing element-wise and matrix operations; utilizing ImageJ/Fiji's math menu and macro functionalities.

5. **Frequency Domain Analysis**: Fourier series and transforms, convolution theorem, sampling theory, and FFT-based filtering.

6. **Advanced Filtering Techniques**: Removing unwanted frequencies and noise; plugin selection for filtering and deconvolution in ImageJ/Fiji.

7. **Temporal and Spatial Image Stacks**: Managing time-lapse images and 3D image stacks in ImageJ/Fiji.

8. **Three-Dimensional Image Analysis**: 3D volumes and corresponding tools in ImageJ/Fiji.

9. **Macro Automation**: Using Fiji's Macro Recorder for task automation.

10. **Object Dynamics Analysis**: Manual versus automatic tracking; particle tracking methodologies.

11. **Image Registration**: Geometrical transformations, affine mappings, and similarity quantification between images; applications in ImageJ/Fiji.

12. **Introduction to Democratic Deep Learning in Image Processing**.

KEY TAKEAWAYS
- The course aims to enable independent development and optimization of image analysis workflows for scientific research.
- A balanced approach combining foundational knowledge and advanced techniques in image processing is adopted.
- Multiple software and language adaptations are encouraged for increased flexibility in implementation.

NOTE
- The curriculum is subject to change based on ongoing assessments and student needs.
- Students are encouraged to install ImageJ/Fiji on their computers for hands-on exercises.

METHODS OF EVALUATION
Continuous assessment based on individual and group activities, both in-class and as homework assignments.

ADDITIONAL RESOURCES
- Open-source software: [ImageJ/Fiji](https://imagej.net/Fiji)
- Optional Software: Matlab, Labview, C++, Python, Julia, R

The course offers a comprehensive yet flexible approach to mastering the critical skill of scientific image analysis, making it invaluable for individuals engaged in modern scientific research.