Nuclei Count

Project Overview

This project is focused on the development of image processing algorithms to accurately count nuclei within biological images. The primary dataset used is BBBC002, which contains varied images of biological cell nuclei under different conditions.

Technologies Used

• Python: The main programming language used.
• Libraries:
  • Seaborn: For statistical data visualization.
  • NumPy: For numerical operations.
  • Matplotlib: For creating static, interactive, and animated visualizations in Python.
  • Pandas: For data manipulation and analysis.
  • Napari: For interactive visualization of large multi-dimensional images.
  • skimage: For image processing in Python.

Setup and Installation

To run this project, you will need to have Python installed on your system. Clone the repository and install the required dependencies:

git clone https://github.com/your-username/nuclei_count.git
cd nuclei_count
pip install -r requirements.txt

Usage

After setting up the project, you can run the Jupyter notebook to begin counting nuclei in your images. The notebook nuclei_count.ipynb includes detailed steps and code comments to guide you through the process.

Acknowledgments

Special thanks to Dr. Robert Haase from TU Dresden for providing the data used in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.