Open Access

ARTICLE

Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

Zeric Tabekoueng Njitacke¹, Louai A. Maghrabi², Musheer Ahmad^3,*, Turki Althaqafi⁴

1 Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, Buea, P.O. Box 63, Cameroon
2 Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, 22246, Saudi Arabia
3 Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, India
4 Department of Computer Science, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah, 22246, Saudi Arabia

* Corresponding Author: Musheer Ahmad. Email:

Computers, Materials & Continua 2025, 83(1), 917-933. https://doi.org/10.32604/cmc.2025.059640

Received 14 October 2024; Accepted 05 February 2025; Issue published 26 March 2025

Abstract

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map. The map demonstrates remarkable chaotic dynamics over a wide range of parameters. We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map, which allows us to select optimal parameter configurations for the encryption process. Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors, an essential characteristic for effective encryption. The encryption technique is based on bit-plane decomposition, wherein a plain image is divided into distinct bit planes. These planes are organized into two matrices: one containing the most significant bit planes and the other housing the least significant ones. The subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance security. An auxiliary matrix is then generated, comprising the combined bit planes that yield the final encrypted image. Experimental results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical images. As a result, image quality is evaluated using the Structural Similarity Index (SSIM), yielding values close to zero for encrypted images and approaching one for decrypted images. Additionally, the entropy values of the encrypted images are near 8, with a Number of Pixel Change Rate (NPCR) and Unified Average Change Intensity (UACI) exceeding 99.50% and 33%, respectively. Furthermore, quantitative assessments of occlusion attacks, along with comparisons to leading algorithms, validate the integrity and efficacy of our medical image encryption approach.

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Keywords

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