modifiedOtsu 0.1.0

Numba-accelerated local modified Otsu thresholding for 3D images.

🧠 Local Modified Otsu Thresholding

Local Modified Otsu Thresholding is a Numba-accelerated implementation of a local, contrast-adaptive Otsu thresholding method for 3D images.

This algorithm adapts the Otsu threshold within a sliding 3D window, enabling robust segmentation in datasets with spatially varying intensity distributions.

The method is inspired by the segmentation approach described in Subsection 2.2.2 of the article:

Segmentation-based tracking of macrophages in 2D + time microscopy movies inside a living animal

Computers in Biology and Medicine, 153 (2023), 106499

https://doi.org/10.1016/j.compbiomed.2022.106499

If you use this implementation in your research, please cite the original work that inspired this algorithm.

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⚙️ Installation

Install the latest stable version from PyPI:

pip install modifiedOtsu

🚀 Example Usage

import numpy as np
from modifiedOtsu import getMask

# Create sample 3D image
img = np.random.randint(0, 256, size=(10, 10, 10), dtype=np.uint8)

# Get threshold map and binary image
threshold, binary = getMask(img, window_size=(3, 3, 3), delta=0.2)

🖥️ Command Line Usage

You can execute the script with custom parameters:

python runner.py --shape 10 10 10 --window 3 3 3 --delta 0.2

📦 Dependencies

• NumPy
• Numba
• Matplotlib

Install dependencies with:

pip install numpy numba matplotlib

📜 License

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

🤝 Contributing

Contributions are welcome! If you'd like to fix a bug, add a feature, or improve performance, please open a pull request or contact the maintainers.

💬 Contact

For questions, issues, or feedback, open an issue on GitHub.