Embedding Encrypted Data in Medical Images: A Novel Approach Using SLIC and QWT

Steganography is the art and science of hiding secret data within a carrier medium, such as medical images, without altering its perceptible quality. In this research, we propose an advanced medical image steganography technique that combines SLIC segmentation with Quaternion Wavelet Transform (QWT) to securely and robustly embed encrypted patient data into medical images. Our technique addresses the critical need for secure transmission and storage of sensitive medical information, overcoming limitations of traditional methods such as noticeable visual distortions, and vulnerability to various image processing attacks. Initially, the medical image is segmented into foreground and background regions using SLIC, ensuring that the embedding process preserves diagnostically important areas while embedding in less critical regions. QWT is then applied to decompose the image into approximation and detail coefficients, providing a robust framework for data embedding. The confidential patient data undergoes dual encryption using both substitution and transposition ciphers, significantly enhancing its security. This encrypted data is converted into binary form and embedded into the approximation coefficients of the wavelet-transformed medical image. To evaluate the effectiveness of our method, we computed key metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM), demonstrating superior performance over traditional steganography techniques like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Least Significant Bit (LSB) methods. Additionally, we tested the robustness of our steganographic medical images against common attacks, including Gaussian noise, salt-and-pepper noise, rotation, translation, blurring, and compression, using Bit Correct Rate (BCR) as the evaluation metric. Our results show that the proposed method achieves BCR values exceeding 94%, ensuring that sensitive medical data remains secure and intact even under adverse conditions.