scholarly journals A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0957
Author(s):  
Bushra Abdullah Shtayt ◽  
Nur Haryani Zakaria ◽  
Nor Hazlyna Harun
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Medical Information ◽  
Medical Images ◽  
Rapid Development ◽  
Image Steganography ◽  
General Idea ◽  
Sensitive Information ◽  
Least Significant Bit ◽  
Other Information

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images.

Pixel Prediction-Based Image Steganography by Support Vector Neural Network

2020 ◽  
Author(s):  
Reshma V K ◽  
Vinod Kumar R S
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Medical Information ◽  
Similarity Index ◽  
Image Steganography ◽  
Secret Message ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Least Significant Bit ◽  
Prediction Scheme

Abstract Securing the privacy of the medical information through the image steganography process has gained more research interest nowadays to protect the privacy of the patient. In the existing works, least significant bit (LSB) replacement strategy was most popularly used to hide the sensitive contents. Here, every pixel was replaced for achieving higher privacy, but it increased the complexity. This work introduces a novel pixel prediction scheme-based image steganography to overcome the complexity issues prevailing in the existing works. In the proposed pixel prediction scheme, the support vector neural network (SVNN) classifier is utilized for the construction of a prediction map, which identifies the suitable pixels for the embedding process. Then, in the embedding phase, wavelet coefficients are extracted from the medical image based on discrete wavelet transform (DWT) and embedding strength, and the secret message is embedded into the HL wavelet band. Finally, the secret message is extracted from the medical image on applying the DWT. The experimentation of the proposed pixel prediction scheme is done by utilizing the medical images from the BRATS database. The proposed pixel prediction scheme has achieved high performance with the values of 48.558 dB, 0.50009 and 0.9879 for the peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation factor, respectively.

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

scholarly journals A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Roseline Oluwaseun Ogundokun ◽  
Oluwakemi Christiana Abikoye
Keyword(s):  
Medical Information ◽  
Medical Data ◽  
Image Steganography ◽  
Medical Information System ◽  
Least Significant Bit ◽  
Stego Image ◽  
Performance Metric ◽  
Challenging Tasks ◽  
Multimedia Technologies ◽  
Lsb Technique

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.

scholarly journals Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems

Cryptography ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 19
Author(s):  
Mayssa Tayachi ◽  
Saleh Mulhem ◽  
Wael Adi ◽  
Laurent Nana ◽  
Anca Pascu ◽  
...  
Keyword(s):  
Information And Communication Technologies ◽  
Digital Watermarking ◽  
Medical Information ◽  
Medical Images ◽  
Rapid Development ◽  
Communication Technologies ◽  
Medical Environment ◽  
Secret Keys ◽  
Information And Communication ◽  
High Level

Telemedicine applications are more and more used due to the rapid development of digital imaging and information and communication technologies. Medical information which include digital medical images and patient’s information are extracted and transmitted over insecure networks for clinical diagnosis and treatments. Digital watermarking is one of the main approaches used to ensure the security of medical images. Nevertheless, in some cases, the only use of digital watermarking is not sufficient to reach a high level of security. Indeed, the watermark could carry essential patient information and needs to be protected. In such cases, cryptography may be used to protect the watermark and to improve the overall secured management in the medical environment. In this paper, we propose a clone-resistant watermarking approach combining a difference expansion watermarking technique with a cryptographic technique based on secret keys generated by a clone-resistant device called Secret Unknown Ciphers (SUCs). The use of SUCs to sign the watermark enforces the security of medical images during their transfer and storage. Experimental results show that the system provides a high level of security against various forms of attacks.

scholarly journals Using the Sonification for Hardly Detectable Details in Medical Images

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Veturia Chiroiu ◽  
Ligia Munteanu ◽  
Rodica Ioan ◽  
Ciprian Dragne ◽  
Luciana Majercsik
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Direct Problem ◽  
Sound Propagation ◽  
Burgers Equation ◽  
Medical Images ◽  
Image Data ◽  
Surgical Operation ◽  
Inverse Approach ◽  
Surgical Operations

AbstractThe inverse sonification problem is investigated in this article in order to detect hardly capturing details in a medical image. The direct problem consists in converting the image data into sound signals by a transformation which involves three steps - data, acoustics parameters and sound representations. The inverse problem is reversing back the sound signals into image data. By using the known sonification operator, the inverse approach does not bring any gain in the sonified medical imaging. The replication of the image already known does not help the diagnosis and surgical operation. In order to bring gains in the medical imaging, a new sonification operator is advanced in this paper, by using the Burgers equation of sound propagation. The sonified medical imaging is useful in interpreting the medical imaging that, however powerful they may be, are never good enough to aid tumour surgery. The inverse approach is exercised on several medical images used to surgical operations.

scholarly journals Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1590
Author(s):  
Laith Alzubaidi ◽  
Muthana Al-Amidie ◽  
Ahmed Al-Asadi ◽  
Amjad J. Humaidi ◽  
Omran Al-Shamma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Medical Image ◽  
Medical Images ◽  
Image Data ◽  
Learning Approach ◽  
Learning Models ◽  
Annotation Process ◽  
Deep Learning Model

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

scholarly journals Comparing Yiq and Ycbcr Colour Image Transforms for Semi-Fragile Medical Image Steganography

2019 ◽  
Author(s):  
Peter Eze ◽  
Parampalli Udaya ◽  
Robin Evans ◽  
Dongxi Liu
Keyword(s):  
Visible Light ◽  
Spread Spectrum ◽  
Medical Image ◽  
Medical Images ◽  
Ex Vivo ◽  
Tamper Detection ◽  
Image Steganography ◽  
Colour Image ◽  
Image Transforms ◽  
Spread Spectrum Watermarking

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

scholarly journals Medical Images Segmentation Based on Unsupervised Algorithms: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Revella E. A. Armya Armya ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Automatic Process ◽  
X Rays ◽  
Resonance Imaging ◽  
Unsupervised Algorithms

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

scholarly journals Cloud based Secure Storage of Files using Hybrid Cryptography and Image Steganography

2020 ◽  
Vol 8 (6) ◽  
pp. 665-667
Keyword(s):  
Data Encryption ◽  
Image Steganography ◽  
Advanced Encryption Standard ◽  
Sensitive Information ◽  
Least Significant Bit ◽  
Data Encryption Standard ◽  
Secure Storage ◽  
Symmetric Key ◽  
Encryption Algorithms ◽  
High Level

Cloud Computing has made it possible to provide individuals as well as organizations with a utility that is costeffective. It empowers businesses by delivering these services using the internet. Files can be shared through the cloud. These files may contain sensitive information that needs to be kept hidden from anonymous users. This is done using cryptographic algorithms. High level of security can be provided using hybrid cryptography to encrypt the data. Advanced Encryption Standard (AES) and Triple Data Encryption Standard (3DES) are the symmetric key encryption algorithms used to secure. An asymmetric key encryption algorithm, Rivest-Shamir-Adleman (RSA) helps in providing a hybrid cryptography model. The security of the key generated can be further enhanced using image steganography method Least Significant Bit (LSB). These issues regarding the security and its challenges will be addressed in this paper and also analyse the measures to handle it.

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