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Book ChapterDOI

Biomedical Image Security Using Matrix Manipulation and DNA Encryption

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TLDR
A secure and lossless encryption method is developed in this work and various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.
Abstract
Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.

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Citations
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Book ChapterDOI

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

TL;DR: In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented and can be helpful for the researchers to understand the recent developments and drawbacks of the present systems.
Book ChapterDOI

A Biomedical Image Segmentation Approach Using Fractional Order Darwinian Particle Swarm Optimization and Thresholding

TL;DR: In this article, a biomedical image segmentation process using fractional order Darwinian Particle Swarm Optimization (FODPSO) and thresholding is proposed, which is tested both visually and quantitatively and the results speaks itself about the efficiency of the proposed work.
Journal ArticleDOI

A chaotic framework and its application in image encryption

TL;DR: The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it and show the prospects of chaos theory and its usage in the field of data security.
Journal ArticleDOI

A robust image encryption framework based on DNA computing and chaotic environment

TL;DR: In this article, a robust image encryption framework is designed that is based on DNA computing and chaos theory, and the low dimensional chaotic map is used in this work to reduce computational overhead and make the system simpler.
Book ChapterDOI

Image Segmentation Based on Galactic Swarm Optimization

TL;DR: In this article, a new image segmentation method is proposed which is based on galactic swarm optimization, which is a metaheuristic method based on the movement of stars, galaxies and other objects of space.
References
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Proceedings ArticleDOI

Cryptanalysis of a chaotic image encryption method

TL;DR: This paper points out CKBA is very weak to the chosen/known-plaintext attack with only one plain-image, and its security to brute-force ciphertext-only attack is overestimated by the authors.
Journal ArticleDOI

Chaotic Dynamics in Smart Grid and Suppression Scheme via Generalized Fuzzy Hyperbolic Model

TL;DR: In this article, a method to control chaotic behavior of a typical Smart Grid based on generalized fuzzy hyperbolic model (GFHM) is presented, which is designed by solving a linear matrix inequality (LMI).
Journal ArticleDOI

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Journal ArticleDOI

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

TL;DR: A novel method for cell segmentation and identification has been proposed that incorporated marking cells in cuckoo search (CS) algorithm and experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time.
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