Open Access

ARTICLE

Pairwise Reversible Data Hiding for Medical Images with Contrast Enhancement

Isaac Asare Boateng^1,2,*, Lord Amoah², Isogun Toluwalase Adewale³

1 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Isaac Asare Boateng. Email:

Journal of Information Hiding and Privacy Protection 2024, 6, 1-19. https://doi.org/10.32604/jihpp.2024.051354

Received 03 March 2024; Accepted 24 May 2024; Issue published 24 June 2024

Abstract

Contrast enhancement in medical images has been vital since the prevalence of image representations in healthcare. In this research, the PRDHMCE (pairwise reversible data hiding for medical images with contrast enhancement) algorithm is proposed as an automatic contrast enhancement (CE) method for medical images based on region of interest (ROI) and non-region of interest (NROI). The PRDHMCE algorithm strategically enhances the ROI after segmentation using histogram stretching and data embedding. An initial histogram evaluation compares histogram bins with their neighbours to select the bin with the maximum pixel count. The selected bin is set as the point for contrast stretching with enhancement and secret data embedding in the ROI. The remaining data is embedded in the NROI while reducing image distortions. Experimental results show the effectiveness of PRDHMCE in optimally improving image contrast and increasing embedding capacity compared with existing methods based on qualitative and objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), relative contrast error (RCE), relative mean brightness error (RMBE) and mean opinion score (MOS). Additionally, PRDHMCE recovers medical images fully without data loss.

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Keywords

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