Medical Image Communication Research Articles

An n-Dimensional Chaotic Map with Application in Reversible Data Hiding for Medical Images.

The drawbacks of a one-dimensional chaotic map are its straightforward structure, abrupt intervals, and ease of signal prediction. Richer performance and a more complicated structure are required for multidimensional chaotic mapping. To address the shortcomings of current chaotic systems, an n-dimensional cosine-transform-based chaotic system (nD-CTBCS) with a chaotic coupling model is suggested in this study. To create chaotic maps of any desired dimension, nD-CTBCS can take advantage of already-existing 1D chaotic maps as seed chaotic maps. Three two-dimensional chaotic maps are provided as examples to illustrate the impact. The findings of the evaluation and experiments demonstrate that the newly created chaotic maps function better, have broader chaotic intervals, and display hyperchaotic behavior. To further demonstrate the practicability of nD-CTBCS, a reversible data hiding scheme is proposed for the secure communication of medical images. The experimental results show that the proposed method has higher security than the existing methods.

A Novel Memristive Multiscroll Multistable Neural Network with Application to Secure Medical Image Communication

A Novel Memristive Multiscroll Multistable Neural Network with Application to Secure Medical Image Communication

Progressive meaningful visual cryptography for secure communication of grayscale medical images

Progressive meaningful visual cryptography for secure communication of grayscale medical images

Sensitivity of standardised radiomics algorithms to mask generation across different software platforms

The field of radiomics continues to converge on a standardised approach to image processing and feature extraction. Conventional radiomics requires a segmentation. Certain features can be sensitive to small contour variations. The industry standard for medical image communication stores contours as coordinate points that must be converted to a binary mask before image processing can take place. This study investigates the impact that the process of converting contours to mask can have on radiomic features calculation. To this end we used a popular open dataset for radiomics standardisation and we compared the impact of masks generated by importing the dataset into 4 medical imaging software. We interfaced our previously standardised radiomics platform with these software using their published application programming interface to access image volume, masks and other data needed to calculate features. Additionally, we used super-sampling strategies to systematically evaluate the impact of contour data pre processing methods on radiomic features calculation. Finally, we evaluated the effect that using different mask generation approaches could have on patient clustering in a multi-center radiomics study. The study shows that even when working on the same dataset, mask and feature discrepancy occurs depending on the contour to mask conversion technique implemented in various medical imaging software. We show that this also affects patient clustering and potentially radiomic-based modelling in multi-centre studies where a mix of mask generation software is used. We provide recommendations to negate this issue and facilitate reproducible and reliable radiomics.

A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis

A Proposed Deep-Learning-Based Framework for Medical Image Communication, Storage and Diagnosis

MIDOM—A DICOM-Based Medical Image Communication System

Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.

Application of three-dimensional reconstruction in stomatology

Three-dimensional reconstruction (3D) in computer vision, is the process of three-dimensional view information from single or multiple views. In medicine, 3D reconstruction techniques are mainly based on MSCT, magnetic resonance imagin and other output medical digital image communication protocol (DICOM) format files. As a new digital medical technology, 3D reconstruction technology has been widely used in the field of stomatology, using 3D software, we can freely rotate, segment and view the CT scan results from many different angles, so that we can get more clear skull contrast and more information, data in CT scan results can also be processed so that reference markers can be clearly displayed for better diagnosis and treatment [1]. It has the unique preoperation design advantage, reduces the operation time, enhances the operation accuracy.

Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A₅ Lattice Vector Quantization

Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{5}$ </tex-math></inline-formula> Lattice Vector Quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician’s name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50% more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced.

Reversible data hiding scheme using deep learning and visual cryptography for medical image communication

Due to a growth in information sharing and the use of multiple digital technologies, human lifestyles have been delivered into the virtual world. This digital world has seen the use of images in special fields increase dramatically, especially in healthcare. Healthcare services can be delivered remotely through telemedicine, a well-known method of providing quality healthcare for people worldwide. There is the risk of illegal exploitation of medical data when telemedicine applications involve exposing data over open networks. In particular, medical experts should exercise more caution when sharing a patient’s private information. In the proposed model, reversible data concealment is combined with visual cryptography to provide a secure method of exchanging medical images. A cover image is divided into nonoverlapping secret shares using Hadamard matrix. Secret digital imaging communication in medicine (DICOM) image is encoded by a deep learning model and embedded into secret shares for secure medical image sharing. Finally, the DICOM image is fully reversibly extracted with the cover image. In this case, a visual cryptographic design is used to secure the embedded secret shares. Furthermore, the metrics, such as mean squared error, peak-signal–to-noise ratio, and normalized correlations, are evaluated in the suggested scheme and are compared with various research outcomes to determine the performance of the nominated model.

The Dissemination Strategy of an Urban Smart Medical Tourism Image by Big Data Analysis Technology.

The advanced level of medical care is closely related to the development and popularity of a city, and it will also drive the development of tourism. The smart urban medical system based on big data analysis technology can greatly facilitate people's lives and increase the flow of people in the city, which is of great significance to the city's tourism image dissemination and branding. The medical system, with eight layers of architecture including access, medical cloud service governance, the medical cloud service resource, the platform's public service, the platform's runtime service, infrastructure, and the overall security and monitoring system of the platform, is designed based on big data analysis technology. Chengdu city is taken as an example based on big data analysis technology to position the dissemination of an urban tourism image. Quantitative analysis and questionnaire methods are used to study the effect of urban smart medical system measurement and tourism image communication positioning based on big data analysis technology. The results show that the smart medical cloud service platform of the urban smart medical system, as a public information service system, supports users in obtaining medical services through various terminal devices without geographical restrictions. The smart medical cloud realizes service aggregation and data sharing compared to the traditional isolated medical service system. Cloud computing has been used as the technical basis, making the scalability and reliability of the system have unprecedented improvements. This paper discusses how to effectively absorb, understand, and use tools in the big data environment, extract information from data, find effective information, make image communication activities accurate, reduce the cost, and improve the efficiency of city image communication. The research shows that big data analysis technology improves patients' medical experience, improves medical efficiency, and alleviates urban medical resource allocation to a certain extent. This technology improves people's satisfaction with the dissemination of urban tourism images, makes urban tourism image dissemination activities accurate, reduces the cost of urban tourism image dissemination, and improves the efficiency of urban tourism image dissemination. The combination of the two can provide a reference for developing urban smart medical care and disseminating a tourism image.

Confidentiality in medical images through a genetic-based steganography algorithm in artificial intelligence.

Nowadays, image steganography has an important role in hiding information in advanced applications, such as medical image communication, confidential communication and secret data storing, protection of data alteration, access control system for digital content distribution and media database systems. In these applications, one of the most important aspects is to hide information in a cover image whithout suffering any alteration. Currently, all existing approaches used to hide a secret message in a cover image produce some level of distortion in this image. Although these levels of distortion present acceptable PSNR values, this causes minimal visual degradation that can be detected by steganalysis techniques. In this work, we propose a steganographic method based on a genetic algorithm to improve the PSNR level reduction. To achieve this aim, the proposed algorithm requires a private key composed of two values. The first value serves as a seed to generate the random values required on the genetic algorithm, and the second value represents the sequence of bit locations of the secret medical image within the cover image. At least the seed must be shared by a secure communication channel. The results demonstrate that the proposed method exhibits higher capacity in terms of PNSR level compared with existing works.

An efficient spatial transformation-based entropy retained reversible data hiding scheme in encrypted images

A critical issue with the current communication revolution is data security and privacy, which is an inevitable part of trustworthiness in the communication system. Hence, the applicability of the reversible data hiding schemes (RDH) in this scenario is encouraging and critical. Like medical image communication, satellite image transmission, etc. Earlier, we explored Arnold transform in one of our previous works to hide the secret data that uses the convolutional neural network (CNN) model to design a complete RDH scheme. The proposed scheme follows a statistical approach to support recovering the cover image and the embedded information. This approach proves advantageous over the previous work following its computational capability. The scheme designed can retain the entropy of the encrypted images even after embedding the additional information, complementing the security of the encryption algorithm.

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

Telemedicine has been intensely promoted in the present pandemic situation of COVID-19 to maintain a strategic distance from the infected person. Several medical tests were used to detect the coronavirus, including antigen, RT-PCR, and a lung CT scan. Only a lung CT-Scan can detect the coronavirus and provide information about the lung infection. As a result, digital imaging plays a critical role in the current pandemic situation. Teleradiology allows for the communication of digital medical images of patients over the internet for diagnosis. A lung CT-Scan test is currently being performed on billions of people to detect COVID-19. These images were sent via the internet for diagnosis and research purposes. The NIfTI image file (.nii extension) was created by the CT-Scanner and contains multiple slices of the lungs. As a result, radiologists determine that the received image has not been tempered during transmission, posing a critical authenticity problem when transmitting these images over the internet. As a result, the researchers are more concerned about the integrity and authenticity of these images in teleradiology. This paper proposes a blind, robust watermarking scheme for lung CT-Scan NIfTI images to address this issue. We use Otsu’s image segmentation algorithm in the proposed scheme to identify the slice with the least amount of medical information for watermark embedding. The proposed scheme employs the Discrete Shearlet Transform (DST), Lifting Wavelet Transform (LWT), and Schur decomposition to embed the encrypted watermark. Watermarks are encrypted using the Affine Transform. The experimental results show that watermarked slice has been tainted by the addition of various sorts of noise, including salt-and-pepper noise, compression, Gaussian noise, speckle noise, and motion blur. After an attack, a watermark is retrieved, and the NC values of extracted watermarks are 0.99623 for Salt and pepper noise, 0.96964 for Gaussian noise, 0.99014 for Speckle noise. The proposed scheme was put to the test with a variety of attacks and produced significant results.

A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution. Digital image authentication can be verified with image watermarking and image encryption schemes. These schemes are widely used to protect images against forgery attacks, and they are useful for protecting copyright and rightful ownership. Depending on the desirable applications, several image encryption and watermarking schemes have been proposed to moderate this attention. This framework presents a new scheme that combines a Walsh Hadamard Transform (WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding (DRPE). First, on the sender side, the secret medical image is encrypted using DRPE. Then the encrypted image is watermarking based on WHT. The combination between watermarking and encryption increases the security and robustness of transmitting an image. The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Normalized cross-correlation (NC), and Feature Similarity Index (FSIM).

A Fractional Fourier Based Medical Image Authentication Approach

Patient medical information in all forms is crucial to keep private and secure, particularly when medical data communication occurs through insecure channels. Therefore, there is a bad need for protecting and securing the color medical images against impostors and invaders. In this paper, an optical medical image security approach is introduced. It is based on the optical bit-plane Jigsaw Transform (JT) and Fractional Fourier Transform (FFT). Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach. A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and security. To confirm the efficiency of the suggested security approach for secure color medical image communication, various assessments on different color medical images are examined based on different statistical security metrics. Furthermore, a comparative analysis is introduced between the suggested security approach and other conventional cryptography protocols. The simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly secure. It has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

A convolutional neural network model based reversible data hiding scheme in encrypted images with block-wise Arnold transform

The research in the domain of reversible data hiding (RDH) is recently explored in all aspects due to its applications in cloud computing, forensics, and medical image communication. In this manuscript, we introduce a RDH scheme in encrypted images which can provide a high embedding rate without compromising the bit error rate during the message extraction and image recovery. The proposed scheme follows a block-wise data hiding process. If the processed block size is A×A pixels, and if A=2n+1, then the data hider can hide any number from the set {0, 1, …, (A+2n−1)} in that block. We have introduced an Arnold transform-based data hiding process in which each block will be undergoing a series of scrambling processes based on the bit sequence that we need to embed in the selected block. The message extraction and image restoration are carried out at the receiver side using a trained convolutional neural network (CNN) model.

A novel cross cosine map based medical image cryptosystem using dynamic bit-level diffusion

Development in networking technology has made the remote diagnosis and treatment of patients a reality through telemedicine. At the same time, storing and transmitting medical documents over an insecure network has always been a daunting challenge. Literature shows that the existing medical image cryptosystems suffer from serious shortcomings. The present work attempts to address this crucial area and propose a suitable solution. A novel hybrid 2-Dimensional Cross Cosine Map (2D-CCM) with an improved chaotic behaviour cryptosystem is proposed. Chaotic series generated by proposed 2D-CCM is utilized in confusion and diffusion architecture. A new dynamic Bit-Flipping in diffusion approach increases the complexity to achieve good encryption results. Simulation and security level assessment is carried out by executing Statistical and differential attack analysis, key sensitivity and exhaustive attack analysis. Robustness is evidenced by the result of noise and crop attack analysis. In addition, SHA-256 is utilized to feed the seed key value of 2D-CCM to resist plaintext attacks. All the assessment and comparison results illustrate that the cipher possesses enhanced security and efficiency than the existing state of the art. Hence the proposed cipher is absolutely apt for secure medical image communication.

Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies.

BackgroundThere has been an increasing trend toward pulmonary segmentectomies to treat early-stage lung cancer, small intrapulmonary metastases, and localized benign pathology. A complete preoperative understanding of pulmonary anatomy is essential for accurate surgical planning and case selection. Identifying intersegmental divisions is extremely difficult when performed on computed tomography. For the preoperative planning of segmentectomies, virtual reality (VR) and artificial intelligence could allow 3-dimensional visualization of the complex anatomy of pulmonary segmental divisions, vascular arborization, and bronchial anatomy. This technology can be applied by surgeons preoperatively to gain better insight into a patient's anatomy for planning segmentectomy.MethodsIn this prospective observational pilot study, we aim to assess and demonstrate the technical feasibility and clinical applicability of the first dedicated artificial intelligence-based and immersive 3-dimensional-VR platform (PulmoVR; jointly developed and manufactured by Department of Cardiothoracic Surgery [Erasmus Medical Center, Rotterdam, The Netherlands], MedicalVR [Amsterdam, The Netherlands], EVOCS Medical Image Communication [Fysicon BV, Oss, The Netherlands], and Thirona [Nijmegen, The Netherlands]) for preoperative planning of video-assisted thoracoscopic segmentectomies.ResultsA total of 10 eligible patients for segmentectomy were included in this study after referral through the institutional thoracic oncology multidisciplinary team. PulmoVR was successfully applied as a supplementary imaging tool to perform video-assisted thoracoscopic segmentectomies. In 40% of the cases, the surgical strategy was adjusted due to the 3-dimensional-VR–based evaluation of anatomy. This underlines the potential benefit of additional VR-guided planning of segmentectomy for both surgeon and patient.ConclusionsOur study demonstrates the successful development and clinical application of the first dedicated artificial intelligence and VR platform for the planning of pulmonary segmentectomy. This is the first study that shows an immersive virtual reality-based application for preoperative planning of segmentectomy to the best of our knowledge.

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.

SLT based watermarking system for secure telemedicine

In telemedicine, communication of medical images through emancipated systems similar to web is transcendent. While communicating, the medical images might be tampered by intruders. The specialist comprehensively hinge on the content of region of interest (ROI) in medical image for electing healing processes. To flee specious diagnosis, the professional needs to authorize the integrity of region of interest of the image formerly making diagnostic conclusions. Based on Slantlet transform (SLT) and singular value decomposition (SVD), this paper exhibits a novel watermarking framework for medical images to spot the altered areas inside ROI and reinstate the ROI. Tests directed utilizing this novel system exhibit that modified zones of ROI are recognized unequivocally and ROI of medicinal picture is reestablished.