ChameleonIQ Documentation

Welcome to ChameleonIQ

ChameleonIQ is a comprehensive Python tool for automated NEMA NU 2-2018 Image Quality analysis of PET/CT systems. It provides phantom detection, segmentation, quality metrics computation, and professional reporting capabilities.

Getting Started

Python API

Additional Resources

Key Features

NEMA NU 2-2018 and NU 4-2008 Compliance: Automated image quality assessment following international standards
Phantom Detection: Intelligent automatic segmentation of NEMA phantoms
Quality Metrics: Percent Contrast, Background Variability, Recovery Coefficient, and Spillover Ratio
Interactive Tools: ROI editor with visualization utilities
Report Generation: Professional PDF/HTML reports with statistical summaries
Batch Processing: CLI for high-throughput analysis
Flexible Configuration: YAML-based configuration system

Installation

Install the latest version:

pip install ChameleonIQ

For development installation:

git clone https://github.com/EdAlita/ChameleonIQ
cd ChameleonIQ
pip install -e .

Citation

If you use ChameleonIQ in your research, please cite:

@software{ulin2026chameleoniq,
    author = {Ulin-Briseno, Edwing Y.},
    title = {ChameleonIQ: NEMA Image Quality Analysis Tool},
    year = {2026},
    url = {https://github.com/EdAlita/ChameleonIQ}
}

Support & Contribution

Repository: GitHub
Issues: Report Bugs
License: MIT AND (Apache-2.0 OR BSD-2-Clause)

Acknowledgements

ChameleonIQ is developed at the Institute for Instrumentation in Molecular Imaging (i3M), a joint research center of the Universitat Politècnica de València (UPV) and the Spanish National Research Council (CSIC).

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