Metadata-Version: 2.4
Name: phydcm
Version: 2.4.0
Summary: An AI-powered DICOM viewer for CT, MRI, and PET images built by medical physics students.
Home-page: https://github.com/PhyDCM/PhyDCM
Author: PhyDCM Team
Author-email: phydcm.team@outlook.com
Project-URL: Documentation, https://github.com/PhyDCM/PhyDCM/wiki
Project-URL: Bug Tracker, https://github.com/PhyDCM/PhyDCM/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: PyQt6>=6.0.0
Requires-Dist: numpy>=1.24
Requires-Dist: matplotlib>=3.7
Requires-Dist: pydicom>=2.4
Requires-Dist: opencv-python>=4.9
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary


# PhyDCM - Medical Imaging AI

![Banner](assets/panner.jpg)

**PhyDCM** is a research-driven project developed by a passionate team of Medical Physics students from the University of Al-Qadisiyah.  
Our aim is to create cutting-edge tools that utilize artificial intelligence to enhance medical image processing and analysis.

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## 👨‍🔬 Team Members

- **Mohammed Hadi**
- **Mohammed Hassan**
- **Haider Ali**
- **Ali Hussein**
  
- **Supervised by Dr. Hayder Saad**

Department of Medical Physics – University of Al-Qadisiyah

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## 🧠 Project Vision

To build smart, accessible software tools that assist doctors and radiologists in analyzing DICOM medical images, using interactive visualization and AI-based diagnostics.

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## 📷 Logo

![Logo](assets/logo.jpg)

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## 📂 Features

- DICOM viewer with interactive video browsing.
- Multi-modality support: CT, MRI, PET.
- AI-ready design for segmentation and classification.
- Clean, user-friendly UI.

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## 🚀 Getting Started

1. Clone the repo
2. Install requirements
3. Run the app

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## 📦 Installation via pip

```bash
pip install phydcm
