scholarly journals Improved Chaos-Based Cryptosystem for Medical Image Encryption and Decryption

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Mohamed Gafsi ◽  
Nessrine Abbassi ◽  
Mohamed Ali Hajjaji ◽  
Jihene Malek ◽  
Abdellatif Mtibaa
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Medical Images ◽  
Multimedia Data ◽  
High Entropy ◽  
Encrypted Image ◽  
Medical Sector ◽  
Internal Condition ◽  
And Performance ◽  
High Level

In the medical sector, the digital image is multimedia data that contain secret information. However, designing an efficient secure cryptosystem to protect the confidential images in sharing is a challenge. In this work, we propose an improved chaos-based cryptosystem to encrypt and decrypt rapidly secret medical images. A complex chaos-based PRNG is suggested to generate a high-quality key that presents high randomness behaviour, high entropy, and high complexity. An improved architecture is proposed to encrypt the secret image that is based on permutation, substitution, and diffusion properties. In the first step, the image’s pixels are randomly permuted through a matrix generated using the PRNG. Next, pixel’s bits are permuted using an internal condition. After that, the pixels are substituted using two different S-boxes with an internal condition. In the final step, the image is diffused by XORing pixels with the key stream generated by the PRNG in order to acquire an encrypted image. R rounds of encryption can be performed in a loop to increase the complexity. The cryptosystem is evaluated in depth by his application on several medical images with different types, contents, and sizes. The obtained simulation results demonstrate that the system enables high-level security and performance. The information entropy of the encrypted image has achieved an average of 7.9998 which is the most important feature of randomness. The algorithm can take full advantage of parallelism and pipeline execution in hardware implementation to meet real-time requirements. The PRNG was tested by NIST 800-22 test suit, which indicates that it is suitable for secure image encryption. It provides a large key space of 2192 which resists the brute-force attack. However, the cryptosystem is appropriate for medical image securing.

An Efficient Machine Learning Based Image Encryption Scheme for Medical Image Security

2021 ◽  
Vol 11 (6) ◽  
pp. 1533-1540
Author(s):  
Shankar Arumugam ◽  
Kannammal Annadurai
Keyword(s):  
Machine Learning ◽  
Medical Image ◽  
Medical Images ◽  
Multimedia Data ◽  
Security Level ◽  
Congruential Generator ◽  
Linear Congruential Generator ◽  
High Level ◽  
Linear Congruential ◽  
Encryption Method

Medical images are very important in most of the application than any other images. In real time applications like telemedicine application, communication of medical image through open access needs protection as well as security at high level. Many imaging information has its own unique features which are so difficult to analyse and make decision to identify necessary techniques for protecting confidential image of unauthenticated access, Utmost all the existing encryption algorithms are mainly concentrating on textual data, but for multimedia data like images, it is not suitable. The main contribution done in this work is for ensuring increased security level over medical images regardless of presence of noises. In this algorithm, DNA subsequence operations combining with the use of improved Combined Linear Congruential Generator (C-LCG) were used for encryption of information. This paper discuss the idea of the improvement of safe and secure techniques using machine learning which is justified by the entropy value and correlation among adjacent pixels with performance parameters. The original image was scrambled using Combined Linear Congruential Generator with Bit rotation operation (BRO) and then image is transformed by effective encryption method using DNA subsequence operations. The proposed scheme discloses the correlation between pixel and entropy. Experimentation results showed that correlation among pixels is reduced while maximizing entropy. Number of Pixel Change Rate (NPCR) and Peak signal to noise (PSNR) ratio were also been analysed. In proposed algorithm, maximum NPCR values is achieved which shows the scattering of pixels in Encrypted image is high. PSNR shows a better encryption quality with lower the values.

Medical Image Encryption

2019 ◽  
pp. 278-304 ◽  
Author(s):  
Sundararaman Rajagopalan ◽  
Siva Janakiraman ◽  
Amirtharajan Rengarajan
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Diffusion Processes ◽  
Medical Images ◽  
Logistic Map ◽  
Healthcare Industry ◽  
Encryption Scheme ◽  
Dna Coding ◽  
Simultaneous Processing ◽  
Image Type

The healthcare industry has been facing a lot of challenges in securing electronic health records (EHR). Medical images have found a noteworthy position for diagnosis leading to therapeutic requirements. Millions of medical images of various modalities are generally safeguarded through software-based encryption. DICOM format is a widely used medical image type. In this chapter, DICOM image encryption implemented on cyclone FPGA and ARM microcontroller platforms is discussed. The methodology includes logistic map, DNA coding, and LFSR towards a balanced confusion – diffusion processes for encrypting 8-bit depth 256 × 256 resolution of DICOM images. For FPGA realization of this algorithm, the concurrency feature has been utilized by simultaneous processing of 128 × 128 pixel blocks which yielded a throughput of 79.4375 Mbps. Noticeably, the ARM controller which replicated this approach through sequential embedded “C” code took 1248 bytes in flash code memory and Cyclone IV FPGA consumed 21,870 logic elements for implementing the proposed encryption scheme with 50 MHz operating clock.

scholarly journals Medical Image Classification Based On Normalized Coding Network with Multiscale Perception

2019 ◽  
Vol 8 (11) ◽  
pp. 2694-2697
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Medical Images ◽  
Vital Role ◽  
Multi Scale ◽  
Proposed Model ◽  
Dataset Size ◽  
High Resolution Images ◽  
High Level

Medical imaging classification is playing a vital role in identifying and diagnoses the diseases, which is very helpful to doctor. Conventional ways classify supported the form, color, and/or texture, most of tiny problematic areas haven’t shown in medical images, which meant less efficient classification and that has poor ability to identify disease. Advanced deep learning algorithms provide an efficient way to construct a finished model that can compute final classification labels with the raw pixels of medical images. These conventional algorithms are not sufficient for high resolution images due to small dataset size, advanced deep learning models suffer from very high computational costs and limitations in the channels and multilayers in the channels. To overcome these limitations, we proposed a new algorithm Normalized Coding Network with Multi-scale Perceptron (NCNMP), which combines high-level features and traditional features. The Architecture of the proposed model includes three stages. Training, retrieve, fuse. We examined the proposed algorithm on medical image dataset NIH2626. We got an overall image classification accuracy of 91.35, which are greater than the present methods.

Medical Image Encryption

2021 ◽  
pp. 269-293
Author(s):  
Sundararaman Rajagopalan ◽  
Siva Janakiraman ◽  
Amirtharajan Rengarajan
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Diffusion Processes ◽  
Medical Images ◽  
Logistic Map ◽  
Healthcare Industry ◽  
Encryption Scheme ◽  
Dna Coding ◽  
Simultaneous Processing ◽  
Image Type

The healthcare industry has been facing a lot of challenges in securing electronic health records (EHR). Medical images have found a noteworthy position for diagnosis leading to therapeutic requirements. Millions of medical images of various modalities are generally safeguarded through software-based encryption. DICOM format is a widely used medical image type. In this chapter, DICOM image encryption implemented on cyclone FPGA and ARM microcontroller platforms is discussed. The methodology includes logistic map, DNA coding, and LFSR towards a balanced confusion – diffusion processes for encrypting 8-bit depth 256 × 256 resolution of DICOM images. For FPGA realization of this algorithm, the concurrency feature has been utilized by simultaneous processing of 128 × 128 pixel blocks which yielded a throughput of 79.4375 Mbps. Noticeably, the ARM controller which replicated this approach through sequential embedded “C” code took 1248 bytes in flash code memory and Cyclone IV FPGA consumed 21,870 logic elements for implementing the proposed encryption scheme with 50 MHz operating clock.

Data Tagging In Medical Images: A Survey of the State-of-art

2020 ◽  
Vol 16 ◽  
Author(s):  
Jyotismita Chaki ◽  
Nilanjan Dey
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Medical Data ◽  
Future Research ◽  
Research Directions ◽  
Data Intensive ◽  
Different Types ◽  
Future Research Directions ◽  
High Level ◽  
State Of Art

: A huge amount of medical data is generated every second, and a significant percentage of them are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the low-level characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.

scholarly journals Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm

Tomography ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 59-76
Author(s):  
Bing Li ◽  
Shaoyong Wu ◽  
Siqin Zhang ◽  
Xia Liu ◽  
Guangqing Li
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Nearest Neighbor ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Image Size ◽  
Texture Information ◽  
Original Size ◽  
And Performance

Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.

scholarly journals Image Encryption Using a Lightweight Stream Encryption Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Saeed Bahrami ◽  
Majid Naderi
Keyword(s):  
Correlation Analysis ◽  
Image Encryption ◽  
Multimedia Data ◽  
Encryption Algorithm ◽  
Security Level ◽  
Visual Test ◽  
Differential Analysis ◽  
Real Time Applications ◽  
And Performance ◽  
Present Algorithm

Security of the multimedia data including image and video is one of the basic requirements for the telecommunications and computer networks. In this paper, we consider a simple and lightweight stream encryption algorithm for image encryption, and a series of tests are performed to confirm suitability of the described encryption algorithm. These tests include visual test, histogram analysis, information entropy, encryption quality, correlation analysis, differential analysis, and performance analysis. Based on this analysis, it can be concluded that the present algorithm in comparison to A5/1 and W7 stream ciphers has the same security level, is better in terms of the speed of performance, and is used for real-time applications.

Medical Signal Security Enhancement Using Chaotic Map and Watermarking Technique

2018 ◽  
pp. 350-370
Author(s):  
Ajita Sahay ◽  
Chittaranjan Pradhan ◽  
Amandip Sinha
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Chaotic Map ◽  
Personal Data ◽  
New Approach ◽  
Encrypted Image ◽  
Security Enhancement ◽  
Medical Signal

This chapter explores medical signal security enhancement using chaotic map and watermarking techniques. This new approach provides security to both the medical image and also maintains the confidentially of both the patient and doctor. Medical image encryption is done by using 2D Gaussian iterated map and BARCODE ECC200. Personal data is encoded in barcode. The encrypted image and barcode are embedded using DCT and DWT, which provides high PSNR values and higher NC value, which help to provide more security.

scholarly journals Multi-Level of Feature Extraction and Classification for X-Ray Medical Image

2018 ◽  
Vol 10 (1) ◽  
pp. 154 ◽  
Author(s):  
Mohammed Muayad Abdulrazzaq ◽  
Imad FT Yaseen ◽  
SA Noah ◽  
Moayad A. Fadhil
Keyword(s):  
Feature Extraction ◽  
Medical Image ◽  
Medical Images ◽  
Image Features ◽  
X Ray ◽  
Extraction Algorithm ◽  
Semantic Concepts ◽  
Automatic Mechanism ◽  
High Level

There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training. Therefore, effective techniques for medical image retrieval and classification are required to provide accurate search through substantial amount of images in a timely manner. Given the amount of images that are required to deal with, it is a non-viable practice to manually annotate these medical images. Additionally, retrieving and indexing them with image visual feature cannot capture high level of semantic concepts, which are necessary for accurate retrieval and effective classification of medical images. Therefore, an automatic mechanism is required to address these limitations. Addressing this, this study formulated an effective classification for X-ray medical images using different feature extractions and classification techniques. Specifically, this study proposed pertinent feature extraction algorithm for X-ray medical images and determined machine learning methods for automatic X-ray medical image classification. This study also evaluated different image features (chiefly global, local, and combined) and classifiers. Consequently, the obtained results from this study improved results obtained from previous related studies.

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