scholarly journals Steganography Technique Based on WPT and ElGamal Encryption with Confusion for Robust Medical Image

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2239-2248
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Wavelet Packet ◽  
Region Of Interest ◽  
Image Data ◽  
Third Party ◽  
Medicine Service ◽  
Health Related ◽  
Elgamal Encryption ◽  
Embedding Rate

For delivering effective health medical images and Electronic Patient Record (EPR) play an important role and these are stored in cloud, remote medical care and tele medicine service. For health care system, all the medical image data are stored in third party a server that is cloud. So, there is more chance to process or change the medical images as well as patient’s records which leads to health-related issues. To prevent the medical details from the hackers, many techniques are proposed and analyzed by the researchers. Anyway, data corruption is done by the attackers till now. In order to improve the security for data, this paper proposes a steganography technique which embed the important details into the medical image by using Wavelet Packet Transform (WPT) without affecting Region of Interest (ROI) which is useful for further diagnosis. Before embedding the patient’s record, these data are encrypted by using ElGamal Encryption technique which provides more security to the data. It is observed from the simulation results that the proposed technique produces better performance in terms of MSE, PSNR and WPSNR values. The PSNR value of the proposed system can increase 8.8%, 6.2%, 12.5%, 9.6%, 6.7% and 6.9% for embedding rate 5%, 10%, 20%, 25%, 30% and 40% respectively from the existing (DWT-ElGamal) technique.

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2021 ◽  
pp. 632-653
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Singular Value Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

Multiple Watermarks Algorithm for Medical Image Based on Arnold Scrambling and DFT

2013 ◽  
Vol 380-384 ◽  
pp. 4116-4119
Author(s):  
Miao Sui ◽  
Jing Bing Li ◽  
Yu Cong Duan
Keyword(s):  
Medical Image ◽  
Feature Vector ◽  
Region Of Interest ◽  
Third Party ◽  
Geometric Attacks ◽  
Arnold Scrambling ◽  
Network Transmission ◽  
Multiple Watermarks ◽  
Medical Accident ◽  
Accident Case

In the process of network transmission, when an exception occurs (such as forgery, tampering, information confusion), digital medical image, as a diagnostic basis, can not serve as the evidence of medical accident case. And the ROI of medical image is unable to tolerate significant changes. In order to deal these problems, we have proposed a multiple watermarks algorithm that uses Arnold scrambling to preprocess the original multiple watermarks, improving the security of watermarking, and combining the visual feature vector of image with the encryption technology and the concept of third-party. Moreover, the sophisticated process is needless to find the Region of Interest (ROI) of medical images. So compared with the existing medical watermarking techniques, it can embed much more data, with less complexity. The experimental results show that the scheme has strong robustness against common attacks and geometric 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 Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
R. Eswaraiah ◽  
E. Sreenivasa Reddy
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Tamper Detection ◽  
High Quality ◽  
Medical Image Watermarking ◽  
Block Based ◽  
Embedding Distortion

In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.

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.

An Imperceptible Watermarking Scheme for Medical Images Tamper Detection

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Secure Communication ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Chaotic Map ◽  
Region Of Interest ◽  
Digital Image Watermarking ◽  
Watermark Image ◽  
Speed Up ◽  
Watermarking Scheme

One of the important issues in telemedicine field refers to an advanced secure communication. Digital image watermarking is an ideal solution since it protects the electronic patient information’s from unauthorized access. This paper presents a novel blind fragile-based image watermarking scheme in spatial domain that merges Speed Up Robust Features (SURF) descriptor with the well-known Weber Descriptors (WDs) and Arnold algorithm. It provides a good way for enhancing the image quality and time complexity for medical data integrity. Firstly, the watermark image is shuffled using Arnold chaotic map. Secondly, the SURF technique is practiced to Region of Interest (ROI) of the medical image and then the blocks around the SURF points are selected to insert the watermark. Finally, the watermark is encrusted and extracted using WDs. Experimental results show good image fidelity with the shortest execution time to ensure medical images integrity.

scholarly journals An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 1063-1078
Author(s):  
P. Sreenivasulu ◽  
S. Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Set Partitioning ◽  
Discrete Wavelet ◽  
Huffman Encoding ◽  
Encoding Method

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

scholarly journals Hardware Implementation of Hybrid Model Encryption Algorithm for Secure Transmission of Medical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 164-167
Keyword(s):  
Hybrid Model ◽  
Medical Image ◽  
Hardware Implementation ◽  
Medical Images ◽  
Image Data ◽  
Encryption Algorithm ◽  
Advanced Encryption Standard ◽  
Medical Field ◽  
Secure Transmission ◽  
Real Time Application

The advancement of medical field needs a more secure function for sharing the medical related images in the present environment. For making secure transmission of medical images the best solution is cryptography algorithm. There are many cryptography algorithm existing in the market out of the entire algorithm we are select the best encryption algorithm which is really suitable for medical images transmission. Transmission of data is easy at the same time secure transmission are meet the different challenges. To full fill the all the challenges we concentrating the encryption algorithm which is highly secure. For making more secure transmission hybrid model encryption algorithm support more compare to single encryption algorithm. Its having capable of providing confidentiality, authenticity and integrity services to medical images exchanged in telemedicine applications. The same hybrid model encryption may implement in real time application using FPGA device. While implementing in hardware the following factors need to be concentrate more such as power, area, throughput, PSNR, Sensitivity etc. keeping all the factors in mind the hybrid model encryption algorithm are developed for secure transmission of medical images. The aim of the research is to encrypt and decrypt medical images efficiently and effectively protect the transmitted data. This research paper presents a model for encrypting transmitted medical image data. This model uses the following encryption algorithm such as Advanced Encryption Standard, Rivest Cipher 4.

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