Transmission Of Medical Images Research Articles

Lucas Algorithm for Medical Images Encryption and Transmission using Orthogonal Frequency Division Multiplexing for Medical Health Information Systems and its VLSI Realization

<p>Security of medical images is critical for transmission of medical images and relevant patient information through public health information systems networks. Image encryption is an important tool for protecting Medical images by converting them into unrecognisable formats for the transmission and receiption of medical images. This paper proposes an algorithm for medical image encryption using Lucas Algorithm, and a discrete wavelet transform based transmission and reception technique, which is known as orthogonal frequency division multiplexing(OFDM). This paper presents an implementation of Lucas Algorithm for image encryption and combines it with Discrete wavelet transform orthogonal frequency division multiplexing using Field Programmable Gate Array.</p>

Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55 to 70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique.

ROI-based medical image watermarking for accurate tamper detection, localisation and recovery.

Smart healthcare systems play a vital role in the current era of Internet of Things (IoT) and Cyber-Physical Systems (CPS); i.e. Industry 4.0. Medical data security has become the integral part of smart hospital applications to ensure data privacy and patient data security. Usually, patient medical reports and diagnostic images are transferred to the specialist physician in other hospitals for effective diagnostics. Therefore, the transmission of medical data over the internet has attained significant interest among many researchers. The three main challenges associated with the e-healthcare systems are the following: (1) ensuring authentication of medical information; (2) transmission of medical image and patient health record (PHR) should not cause data mismatch/detachment; and (3) medical image should not be modified accidentally or intentionally as they are transmitted over the insecure medium. Thus, it is highly essential to ensure the integrity of the medical image, especially the region of interest (ROI) before taking any diagnostic decisions. Watermarking is a well-known technique used to overcome these challenges. The current research work has developed a watermarking algorithm to ensure integrity and authentication of the medical data and image. In this paper, a novel watermarking algorithm is designed based on Integer Wavelet Transform (IWT), combined chaotic map, recovery bit generation and SHA-256 to address the objective as mentioned earlier. The paper's significant contribution is divided into four phases, namely, watermark generation and data embedding phase, authentication phase, tamper detection phase and localisation and lossless recovery phase. Experiments are carried out to prove that the developed IWT-based data embedding scheme offers high robustness to the data embedded in region of non-interest (RONI), detects and localises the tampered blocks inside ROI with 100% accuracy and recovers the tampered segments of ROI with zero MSE. Further, a comparison is made with the state-of-art schemes to verify the sternness of the developed system.

Medical images transmission over Wireless Multimedia Sensor Networks with high data rate

This paper presents several proposed scenarios for medical image transmission over Wireless Multimedia Sensor Networks with multi-level security tools and Unequal Error Protection (UEP) technique to provide highly secure and improved medical images transmitted over the wireless link. The proposed scenario is based on embedding the medical images with audio watermarking technique. The audio signal is presented as a medical report for the embedded medical image. The audio watermarking in this paper used the Singular Value Decomposition technique to hide the image as a watermark into the audio cover signal to form the watermarked signal. This combined signal is transmitted using the UEP technique by employing different error control schemes and different packet lengths to enhance the quality of extracting encrypted/unencrypted medical images. The results show that the quality of the extracted medical images with encoded packets is better, also, the encryption of different error control schemes employing with the image enhances the extracted image and its quality.

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks.

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

An efficient watermarking algorithm for medical images

Medical images exchanged over vulnerable networks are sensitive to modifications or tampering which could lead to misdiagnosis, risking the lives of the patients. Therefore, to provide safe and secure transmission of medical images, key security requirements such as authenticity, integrity, and confidentiality must be addressed. In this paper, a novel and efficient algorithm is proposed which provides security services for transmitted medical images in tele-medicine applications. The proposed algorithm employs joint encryption/watermarking procedures to provide the required security services, as well as to provide control access privileges at the receiving side. The algorithm is based on reversible data hiding principles that guarantee the extraction of the hidden data while restoring the original image unaffected. The embedded data is basically two watermarks; a spatial domain watermark and an encrypted domain watermark. The dual watermarks allow the authenticity and integrity of the transmitted medical images to be verified in the spatial and encrypted domains. The proposed algorithm has been extensively evaluated using three performance metrics; embedding capacity, visual image quality, and entropy. The ability of the algorithm to fulfill the security, reversibility, and separability requirements has been validated.

A secure medical image transmission scheme aided by quantum representation

Quantum information and quantum image processing have made immense progress in the most recent years. The proposed paper investigates the proficiency of quantum image encryption in the field of Telehealth which can be built by incorporating quantum block-based spatial transformations. The proposed quantum cryptosystem introduced two levels of security for medical image and its sensitive content. The secrecy of medical image is reinforced by choosing a plain image based initial seed values for encryption. It is found that the suggested quantum block-based scrambling can serve as a novel method for intensifying the privacy-preserving process of medical image. It additionally ensures the medical image integrity by introducing dedicated quantum encryption for ROI based regional data. With a bilateral image transmission perspective, the proposed paper introduced InterPlanetary Features based file system for proficient retrieval of medical images. The quantitative simulation and performance evaluation done on the Cancer Imaging Archive dataset manifests that the suggested quantum-based two-level security for medical images is suitable for reinforcing the confidentiality and privacy-preserving process. It also enlarges the effectiveness of quantum image processing applications in Telemedicine.

Quality of service analysis of video streaming in fourth generation-Long Term Evolution for telemedicine applications: An experimental learning with game theory approach

Wireless technologies have become the model for Entertainment, Communication and Education all over the world. Electrical and Electronics Engineering Departments of various Universities across the globe offer courses on Wireless Communication and Networks for Undergraduate and Postgraduate students by focusing on the latest teaching pedagogy. Recent advancements in health care services demand reliable transmission of medical images and videos for better diagnosis and treatment. The effective utilization of available spectrum is one of the requirements for stable transmission of medical information. A Long Term Evolution (LTE) system model for a real-time multimedia application must provide high Quality of Service (QoS). Scheduling and allocating resources for real-time downlink flows in LTE networks is a challenging issue, as the network excellence and assured output have to be confirmed together with decreasing the Head of Line (HOL) suspension. The ever increasing demand for higher data rates in uplink and downlink transmission services constraints on delay and bandwidth requirements. This leads the existing fourth generation LTE wireless networks to concentrate more on the resource scheduling techniques with acceptable levels of Quality of Service (QoS). In this paper, a Game theory based scheduling (GTBS) and resource allocation Algorithm multimedia traffic in LTE network is designed and studied. The Algorithm is simulated in LTE-sim for real time (RT) and Non-real time (NRT) data traffic. The work identified a new teaching pedagogy, the work is explained to the undergraduate Engineering students so that they can apply the knowledge to develop new innovative projects.

A Reversible Data Hiding Scheme through Encryption using Rotated Stream Cipher

The research in the domain of reversible data hiding got much attention in recent years due to its wide applications in medical image transmission and cloud computing. Reversible data hiding during image encryption is a recently emerged framework for hiding secret data into an image during the image encryption process. In this manuscript, we propose a new reversible data hiding through encryption scheme which will ensure a high embedding rate without bringing any additional overhead of key handling. The proposed algorithm can use any secure symmetric encryption scheme, and the encryption and/or decryption key should be shared with the receiver for data extraction and image recovery. As per the proposed scheme, the data hider can hide three-bits of secret message in an image block of size $B\times B$ pixels. The data extraction image recovery will be carried out by analyzing the closeness between adjacent pixels. The simulation of the new scheme on the USC-SIPI dataset shows that the proposed scheme outperforms the well-known existing schemes in embedding rate and bit error rate.

Performance Improvement for Medical Image Transmission Systems using Turbo-Trellis Coded Modulation (TTCM)

Digital images have become an essential working tool in several areas such as the medical field, the satellite and astronomical field, film production, etc. The efficiency of a transmission system to exchange digital images is crucial to allow better and accurate reception. Generally, transmitted images are infected with noise. In the medical field, this noise makes the process of diagnosing difficult. To eliminate the transmission errors, an Error Correcting Code (ECC) can be used with the aim to guarantee excellent reception of the images and allowing a good diagnosis. In this paper, source and channel encoding/ decoding functions are studied during medical image transmission (LUNG). At first, the Turbo-Code (TC) is used as ECC and subsequently the Turbo-Trellis Coded Modulation (TTCM). The simulation results represent the curves giving the Bit Error Rate (BER) as a function of the signal to noise ratio (Eb/N0). In order to compare these two systems properly, the MSSIM (Mean Structural Similarity) parameter was used. The obtained results showed the effect and importance of ECC on the transmission of medical images using TTCM which gave better results compared to TC with regard to increasing the performance of the transmission system by eliminating transmission noise.

Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required to reduce the redundancies in the image and to accomplish the proficient image communication. To reduce the redundancies in the image and to accomplish the proficient communication of images. A competent Adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASFSCSLEC-DNL) technique is presented to develop the image encryption and compression. The main goal of the ASFSCSLEC-DNL technique was to improve the security level of medical image transmission. The deep feed-forward artificial neural network was applied in the ASFSCSLEC-DNL technique for medical image pre-processing, encryption, and compression with multiple layers. The adaptive sigma filter was employed to denoise the medical image. The medical image encryption and signature generation were done with synorr certificateless signcryption. Finally, Levenshtein entropy encoding was applied to compress images. Then the compressed image was sent to the receiver where the decompression and decryption are implemented using Levenshtein entropy decoding and synorr certificateless decryption. Investigational estimation was carried out in chest X-ray medical images and the results of ASFSCSLEC-DNL technique proved more capable in terms of higher peak signal to noise ratio and compression ratio with lesser encryption time compared to the existing state-of-the-art methods.

Component for Renal Carcinoma Detection in Abdominal Tomography applying Watersheds Transform in the XAVIA PACS System

One of the most recognized campaigns in the world is the fight against cancer, being the renal system one of the most affected by this pathology. Renal Cell Carcinoma (RCC), the most common form of kidney cancer in adults, is the sixth leading cause of cancer death. Due to the increased use of imaging techniques, kidney lesions can be diagnosed incidentally in approximately 50% of cases. Cuba bets on the use of technology in health and the University of Informatics Sciences (UCI) has developed a system for the storage, transmission and visualization of medical images (XAVIA PACS), which is deployed in several hospitals in the country, but does not have a component that can perform the detection of RCC in tomographic images, making the diagnosis slower, which translates into fewer possibilities for the patient. An informatic component was developed for the renal carcinomas detection in abdominal tomography images based on the Watersheds Transform. This component supports the diagnosis of RCC by specialists in the health centers where the XAVIA-PACS was deployed. An experiment was developed comparing the results with images from an international Database (KiTS 2019), in addition their diagnosis value was analyzed by leading medical specialists.

Secure Transmission of Medical Image for Telemedicine

Secure Transmission of Medical Image for Telemedicine

Pyramid based Progressive Transmission System with Hybrid Compression Scheme for Medical Images

Transmission of medical images over the internet has become common in telemedicine services. Telemedicine is an enabler for rural health care services where network quality varies over time and there is a need for robustness in presence of poor network quality and efficient use of bandwidth. At the same time, the quality of medical images cannot be compromised with network constraints. In this work, a progressive transmission system with an efficient compression scheme is proposed for the effectual transmission of medical images over the internet with consideration for optimal use of network bandwidth. The image quality can be fine-tuned based on network bandwidth availability and user requirements.

Robust zero-watermarking scheme based on novel quaternion radial fractional Charlier moments

In this paper, we propose a zero-watermarking scheme based on a new set of quaternion moments called Quaternion Radial Fractional Charlier Moments (QRFrCMs) for the copyright protection of color medical images. The proposed moments are developed from a new type of discrete orthogonal polynomials called Fractional Charlier Polynomials (FrCPs) and from quaternion theory. The robustness of the proposed scheme is ensured thanks to robustness of the proposed quaternion moments against image processing attacks and geometric attacks where the BCR is greater than 98%. In addition, the scheme uses the fractional order of QRFrCMs and a chaotic system based on two-dimensional CML (2DCML) using mixed linear-nonlinear coupling to provide a high level of security. Experimental results show the superiority of the proposed scheme over other recent schemes in terms of robustness against geometric attacks and common image processing attacks. The proposed scheme can be used for the secure transmission of color medical images over insecure networks.

Texture maps and chaotic maps framework for secure medical image transmission

Telemedicine has evolved significantly for detection and diagnosis of diseases remotely, which means more frequent transmission of medical images over the network. The security of any medical image can be characterized as its integrity, confidentiality and authentication. It is these security vulnerabilities that limit the development of mobile healthcare applications, which intend to improve the efficiency of medical image communication. To address the vulnerabilities associated with medical images, we propose a texture edge map and multilevel chaotic map driven encryption framework for medical images. This technique utilizes texture maps generated by utilizing a bank of gabor filters along with multiple chaotic maps viz.: Sine, Cubic and Logistic maps for enhancing the key space, robustness and security of medical images over an insecure channel. The security, speed and reliability of the proposed technique for medical images are illustrated via experiments for key sensitivity, statistical and performance analysis. The proposed technique offers a large key space, pixel diffusion at an acceptable speed. Security analysis shows a high sensitive dependence of the encryption and decryption techniques to any subtle change in the secret key, the plain medical image and the encrypted image. Also, the proposed technique has a large enough key space to see off brute force attacks. Therefore, the proposed technique is a potential candidate for addressing security vulnerabilities of medical images over the communication networks.

Medical Image Encryption Algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations

Digital images are now widely used in modern clinic diagnosis. The diagnostic images with confidential information related to patients’ privacy are stored and transmitted via public networks. Secured schemes to guarantee confidentiality of patients’ privacy are becoming more and more vital. Medical images are considered as one of the most important and sensitive data in information systems. Sending medical images over the network requires a strong encryption algorithm such that it is resistant against cryptographic attacks. Among the three security objectives for the security of information systems namely confidentiality, integrity and availability, confidentiality is the most important aspect that need to be taken much care for the secure storage and transmission of medical images. This paper proposes an adaptive medical image encryption algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations that encrypts any medical image with same uniformity and efficiency.

WITHDRAWN: Secure medical image transmission using modified leading diagonal sorting with probabilistic visual cryptography

Informations are being sent through open channels and the security of those informations has been primary considerations. Aside from numerous regular cryptographic techniques, Visual Cryptographic (VC) methods have likewise been being used for information and data security. VC is a secret sharing method as it breaks the original image into share images in order, when the shares are stacked on each other a hidden secret image is uncovered. The VC technique is a secure technique that encodes a secret file or image by breaking it into share images. Cloud computing is a progression model for large scale systems. Security for medical image transmission has been an ongoing computational problem from image recovery to its processing, storage, and retrieval with the advancement of data in the medical sector and health care systems into the cloud. However, the role of Cloud Computing is applied only if security is assured. Modified Leading Diagonal Sorting with Probabilistic Visual Cryptography (MLDS-PVC) is proposed in this research. Breast cancer mammography images from the MIAS dataset are used in this work for medical images. The proposed model could support the transmission of medical images using cloud storage between medical organizations, diagnostics centers, and other health centers.

A Novel Medical Image Encryption using Cyclic Coding in Covid-19 Pandemic Situation

During this Covid - 19 pandemic situation, encryption of medical images takes a major role in medical information systems as well as in telemedicine. However according to government rule, it is essential to hide the information of the patients. Recent development in computer network enhances a lot of facilities in communication area. Unfortunately, hackers are misusing this facility and always try to attack on the transmitted information in insecure network. For secure transmission of medical images, it is essential to encrypt the information before transmitting. In this communication, we are going to present a novel approach of medical image encryption using cyclic coding. We have proved that it is quite difficult to decrypt the original information from encoded data in one common mode of attacking-chosen ciphertext attack. Moreover, we have proved the effectiveness of the encryption using correlation coefficients. Our proposed scheme is suitable for efficient encoding of multiple medical images.

Secure CT-Image Encryption for COVID-19 Infections Using HBBS-Based Multiple Key-Streams.

The task of preserving patient data is becoming more sophisticated with the evolution of technology and its integration with the medical sector in the form of telemedicine and electronic health (e-health). Secured medical image transmission requires adequate techniques for protecting patient privacy. This study aims at encrypting Coronavirus (COVID-19) images of Computed Tomography (CT) chest scan into cipherimages for secure real-world data transmission of infected patients. Provably safe pseudo-random generators are used for the production of a "key-stream" to achieve high privacy of patient data. The Blum Blum Shub (BBS) generator is a powerful generator of pseudo-random bit-strings. In this article, a hashing version of BBS, namely Hash-BBS (HBBS) generator, is presented to exploit the benefits of a hash function to reinforce the integrity of extracted binary sequences for creating multiple key-streams. The NIST-test-suite has been used to analyze and verify the statistical properties of resulted key bit-strings of all tested operations. The obtained bit-strings showed good randomness properties; consequently, uniform distributed binary sequence was achieved over the key length. Based on the obtained key-streams, an encryption scheme of four COVID-19 CT-images is proposed and designed to attain a high grade of confidentiality and integrity in transmission of medical data. In addition, a comprehensive performance analysis was done using different evaluation metrics. The evaluation results of this study demonstrated that the proposed key-stream generator outperforms the other security methods of previous studies. Therefore, it can be successfully applied to satisfy security requirements of transmitting CT-images for COVID-19 patients.