Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Author:

Geetha B. T.1,Mohan Prakash2,Mayuri A. V. R.3,Jackulin T.4,Aldo Stalin J. L.5,Anitha Varagantham6ORCID

Affiliation:

1. Department of ECE, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, India

2. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India

3. SCSE, VIT Bhopal University, Bhopal, India

4. Department of CSE, Panimalar Engineering College, Chennai, India

5. Department of Information Technology, Sona College of Technology, Salem, India

6. Department of Computer Science, Institute of Technology, Hawassa University, Hawassa, Ethiopia

Abstract

Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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