Enhanced Data Security and Integrity using Contourlet Transform for Medical Images

Abstract

Objective: The objective of this framework is to analyze the security, compression ratio and integrity by using segmentation algorithm and transformation to extract the tumor region from MRI brain image and to observe its performance metrics. Methods/Statistical Analysis: This paper offers a futuristic healthcare solution to encompass of segmenting the ROI using Bhattacharya coefficient algorithms and successively applying the modified EMD steganography method centered on contourlet transform. This framework also challenges to verify the integrity of ROI using SHA-1, precisely senses any variation in ROI, furthermore, it ensures robustness of the entrenched data in non-region of interest and mends ROI perfectly for investigation. Lastly the whole image is encrypted with modified Logistic map encryption in order to afford overall security. Findings: This work recapitulates the contrast of distinct embedding algorithms viz. Least Significant Bit - Discrete Cosine Transform (LSB-DCT) and no-shrinkage F5-Integer Wavelet Transform (nsF5-IWT) with Improved Exploiting Modification Direction-Contourlet Transform (IEMD-CT) for embedding processes. The performance analysis of integrity check during transmission is verified using Secure Hash Algorithm 1 (SHA-1). Experimental endings deliberate Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE), Bit Error Rate (BER), Signal to Noise Ratio (SNR) as performance metrics and found the effectiveness of the proposed framework over conventional methods. Application/Improvements: This framework can be exploited in telemedicine applications in order to obtained with meticulousness in tumor location for effective healthcare services. Keywords: Brain Tumor, Confidentiality, Contourlet Transform, Integrity, MRI Image, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Tumor Extraction