CellScope 🔬

A powerful microscopy image analysis tool built with Streamlit for counting and analyzing objects in microscopy images.

Features

• Image Upload: Support for PNG, JPG, JPEG, and TIFF formats
• Noise Reduction: Gaussian filtering with adjustable sigma parameter
• Multiple Thresholding Methods:
  • Isodata
  • Li
  • Mean
  • Minimum
  • Otsu
  • Triangle
  • Yen
  • Manual threshold control
• Object Detection: Automatic counting of objects in images
• Morphological Analysis:
  • Area measurement
  • Eccentricity calculation
  • Statistical summaries
• Visualization: Color-coded labeled images for easy object identification

Installation

1. Clone the repository:

git clone https://github.com/YOUR_USERNAME/cellscope.git
cd cellscope

2. Install the required dependencies:

pip install -r requirements.txt

Usage

Run the application using Streamlit:

streamlit run app.py

The application will open in your default web browser. From there:

1. Upload a microscopy image using the file uploader
2. Adjust the Gaussian filter sigma value in the sidebar
3. Select a thresholding method to segment your image
4. View the detected objects count and their properties
5. Analyze statistical measurements (area, eccentricity)

Requirements

• Python 3.7+
• streamlit
• numpy
• scikit-image
• matplotlib
• pandas

How It Works

1. Image Processing: The uploaded image is converted to grayscale if needed
2. Filtering: A Gaussian filter reduces noise in the image
3. Thresholding: Various algorithms segment the image into foreground (objects) and background
4. Object Detection: Connected components are identified and labeled
5. Quantification: Morphological properties are measured for each detected object

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Acknowledgments

Built with:

• Streamlit - Web application framework
• scikit-image - Image processing library
• NumPy - Numerical computing
• Pandas - Data analysis

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Made with ❤️ for microscopy image analysis