# 🔐 Secure Reversible Data Hiding in Encrypted Medical Images Using Huffman Tree Coding

A research implementation of **Reversible Data Hiding in Encrypted Medical Images (RDHEMI)** using **Hybrid Tree Coding (HTC)** and **Count Encryption (CE)**. The project focuses on secure data embedding in encrypted medical images while ensuring exact recovery of both the hidden payload and the original image.

---

# 📖 Overview

Medical images contain sensitive patient information and require strong security during storage and transmission. This project implements a secure RDHEMI framework that combines **Hybrid Tree Coding (HC + LLC)** with **Count Encryption** to improve compression efficiency and achieve high embedding capacity while preserving reversibility.

---

# 🚀 Key Features

- MED Prediction

- Prediction Error Map Generation

- Huffman Coding (HC)

- Leaf-to-Leaf Coding (LLC)

- Hybrid Tree Coding (HTC)

- Count Encryption

- Reserved Room Generation

- Bit Replacement Data Embedding

- Payload Extraction

- Exact Original Image Recovery

---

# 🔄 Workflow

```text

Medical Image

│

▼

MED Prediction

│

▼

Prediction Error Map

│

▼

Hybrid Tree Coding

│

▼

Count Encryption

│

▼

Reserved Room Generation

│

▼

Bit Replacement

│

▼

Marked Encrypted Image

│

▼

Payload Extraction

│

▼

Perfect Image Recovery

```

---

# 🛠 Technologies Used

- Python

- NumPy

- OpenCV

- Matplotlib

- Scikit-image

- Pillow

---

# 📊 Experimental Results

| Metric | Result |

|---------|--------|

| Embedding Capacity | **1,377,394 bits** |

| Embedding Rate | **5.2543 bpp** |

| PSNR (Original vs Encrypted) | **6.5446 dB** |

| PSNR (Original vs Marked Encrypted) | **6.5462 dB** |

| Entropy | **7.9994** |

| Payload Recovery | ✅ 100% |

| Image Recovery | ✅ 100% |

---

# 📂 Datasets

The implementation was evaluated using publicly available medical image datasets.

- **SIIM Medical Images (CT Images)**

- https://www.kaggle.com/datasets/kmader/siim-medical-images

- **Brain MRI Images for Brain Tumor Detection**

- https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection

- **Labeled Chest X-Ray Images**

- https://www.kaggle.com/datasets/tolgadincer/labeled-chest-xray-images/data

---

# 🎯 Applications

- Secure Medical Image Storage

- Cloud Healthcare Systems

- Electronic Health Records (EHR)

- Telemedicine

- Hospital Information Systems

- Medical Image Archiving

---

# 🔮 Future Scope

- AI-assisted medical image security

- Blockchain-based healthcare systems

- Secure cloud storage

- IoT healthcare applications

---

# 📚 Reference

This implementation is based on the IEEE research paper:

**Reversible Data Hiding in Encrypted Medical Images Based on Huffman Tree Coding and Count-Encryption**

IEEE Xplore:

https://ieeexplore.ieee.org/document/11125529

---

# ⚠️ Disclaimer

This repository contains an independent academic implementation developed for educational and research purposes. It is inspired by the referenced IEEE publication and does not reproduce or redistribute the original copyrighted work.

---

# 👨‍💻 Author

**Krish Gupta**

B.Tech Computer Science (AI & ML)

JSS Academy of Technical Education