DWT-DFT and Logistic Map Based Watermarking Algorithm for Medical Image

2013 ◽  
Vol 380-384 ◽  
pp. 4124-4127
Author(s):  
Yao Li Liu ◽  
Jing Bing Li
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Personal Information ◽  
Logistic Map ◽  
The Internet ◽  
Visual Feature ◽  
Robust Watermarking ◽  
Feature Vectors ◽  
Strong Robustness ◽  
Geometrical Attacks

When medical image transmitted and stored on the Internet, it is vulnerable to many kinds of attacks, thus, the security of the patients personal information is low. A robust watermarking algorithm has been proposed to increase the security of medical images. The scheme obtains the visual feature vectors of the medical image using DWT-DFT. At the same time, applying Logistic Map to encrypt the watermarking image, which can enhance its security. The experiment results show that the scheme has strong robustness against geometrical attacks.

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.

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.

scholarly journals Medical Image Detection & Privacy Management With Elliptic Curve GOPSO Cryptographic Optimization Technique on the Internet of Health Things

2021 ◽  
Author(s):  
Anthony Vincent B ◽  
Cecil Donald A ◽  
B J Hubert Shanthan ◽  
Ankur Singh Bist ◽  
Haider mehraj ◽  
...  
Keyword(s):  
Health Care ◽  
Medical Image ◽  
Medical Images ◽  
Full Range ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
The Internet ◽  
Care Providers ◽  
Clinical Images ◽  
Image Protection

Abstract The advent of the Internet of Things (IoT) is to transform the health care sector and lead to the development of the Internet of Health Things (IoHT). This technology exceeds existing human services mechanically, financially, and socially. This paper used an advanced cryptographic framework that includes optimization strategies to look at IoHT medical image protection. The patient data kept on a cloud server which was detected and sensed from the IoHT Healthcare devices. It's critical to ensure the safety and privacy of patient clinical images in the cloud; here, an enhanced security framework for health information promotes trust. Next, we presented health care providers who could provide the full range of medical facilities for IoHT participants. In the process of encrypting/decrypting elliptical curves, the optimal key is selected using the Grasshopper Particle Swarm Optimization (GOPSO) to increase the security standard of medical images. Medical images are protected within IoHT by using this approach.The implementation results were analyzed and compared with a variety of encryption algorithms and their optimization techniques. The effectiveness of the proposed methods and the results show that the medical image is secure and prevents attacks in IoHT-based health care systems.

scholarly journals 3D DWT-DCT and Logistic MAP Based Robust Watermarking for Medical Volume Data

2014 ◽  
Vol 8 (1) ◽  
pp. 131-141 ◽  
Author(s):  
Jingbing Li ◽  
Yaoli Liu ◽  
Jiling Zhong
Keyword(s):  
Medical Information ◽  
Feature Vector ◽  
Logistic Map ◽  
Third Party ◽  
Volume Data ◽  
Robust Watermarking ◽  
3D Dwt ◽  
Zero Watermarking ◽  
Watermarking Scheme ◽  
Geometrical Attacks

Applying digital watermarking technique for the security protection of medical information systems is a hotspot of research in recent years. In this paper, we present a robust watermarking algorithm for medical volume data using 3D DWT-DCT and Logistic Map. After applying Logistic Map to enhance the security of watermarking, the visual feature vector of medical volume data is obtained using 3D DWT-DCT. Combining the feature vector, the third party concept and Hash function, a zero-watermarking scheme can be achieved. The proposed algorithm can mitigate the illogicality between robustness and invisibility. The experiment results show that the proposed algorithm is robust to common and geometrical attacks.

scholarly journals A Naïve Visual Cryptographic Algorithm for the Transfer of a Compressed Medical Images

2015 ◽  
Vol 3 (4) ◽  
pp. 26
Author(s):  
Shiva Putra ◽  
H. S. Sheshadri ◽  
V. Lokesha
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Logistic Map ◽  
Security Measures ◽  
Efficient Design ◽  
Image Size ◽  
Dicom Standard ◽  
Absolute Deviation ◽  
Deviation Analysis ◽  
Image Pixels

The transmission of a suitably compressed image over a bandwidth, over long distances, gives rise towards a new era in the field of information technology. A gradual increase in this appending scenic application, involving the transfer of the images securely over the Ethernet has become an increasingly important aspect to be addressed during thou phenomenon, especially in the transfer of the digital medical images vividly, encapsulated with abundant information related to these images. The compressed medical images of the DICOM format contain certain amount of confidential data, pertaining to a clinical research or to an individual, and the confidentiality of the same has to be preserved from various security threats and eves-dropping. With a widespread applications among various multimedia applicative systems, telemedicine, medical imaging, military and certain safety-critical applications, inter-net and intra-net communicative applications, etc, a reliable transfer of suitable information, efficiently & securely is considered as one of the revolutionary aims in today’s communication technology and visual cryptographic methodologies. Real-time applications as such detailed above majorly is concerned with the security measures and many algorithms have been developed as a proof for various visual cryptographic methodologies. In this paper we propose an efficient and a reliable visual cryptographic methodology which focuses on the encryption and decryption of the two-dimensional DICOM standard compressed medical image, effectively. This paper discusses an efficient design of 192 bit encoder using AES Rijndael Algorithm with the decomposition of an image into square image size blocks and the image blocks are shuffled using 2D CAT map. The shuffling of the image blocks/pixels employs a Logistic map of these image pixels coupled with 2D mapping of the pixels of the DICOM standard medical image, generated randomly, being the control parameter thereby creating a confusion between the cipher and the plain image, gradually increasing the resistive factor against the significant attacks. This paper proposes various analytical metrics such as correlation analysis, entropy analysis, homogeneity analysis, energy analysis, contrast and mean of absolute deviation analysis, to evaluate the proposed algorithm, and their suitability in image encryption applications.

scholarly journals TransMed: Transformers Advance Multi-Modal Medical Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1384
Author(s):  
Yin Dai ◽  
Yifan Gao ◽  
Fayu Liu
Keyword(s):  
Image Analysis ◽  
Image Classification ◽  
Long Range ◽  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Medical Image Classification

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

scholarly journals Accuracy of internet images of glenoid labral injuries

2013 ◽  
Vol 95 (6) ◽  
pp. 418-420 ◽  
Author(s):  
R Freeman ◽  
F Ashouri ◽  
J Papanikitas ◽  
D Ricketts
Keyword(s):  
Health Information ◽  
Search Engines ◽  
Shoulder Dislocation ◽  
Medical Images ◽  
Interobserver Reliability ◽  
Bankart Lesion ◽  
The Internet ◽  
Glenoid Labrum ◽  
Written Information ◽  
Internet Images

Introduction The internet is a convenient source of health information used widely by patients and doctors. Previous studies have found that the written information provided was often inaccurate. There is no literature regarding the accuracy of medical images on the internet. The aim of this study was to assess the accuracy of internet images of injuries to the glenoid labrum following shoulder dislocation. Methods The Google and Bing search engines were used to find images of Bankart, Perthes and anterior labroligamentous periosteal sleeve avulsion (ALPSA) lesions. Three independent reviewers assessed the accuracy of image labelling. Results Of images labelled ‘Bankart lesion’, 30% (9/30) were incorrect while ‘Perthes lesion’ images were incorrect in 15% of cases (9/60) and 4% of ‘ALPSA lesion’ images were incorrect (2/46). There was good interobserver reliability (kappa = 0.81). Labelling accuracy was better on educational sites than on commercial sites (6% vs 25% inaccurate, p=0.0013). Conclusions Caution is recommended when interpreting non-peer reviewed images on the internet.

Geometrically Invariant NSCT–GPCET-Based Image Watermarking with Accurate Moment Calculation

2021 ◽  
pp. 2150284
Author(s):  
Wenbing Wang ◽  
Shengli Liu ◽  
Liu Feng
Keyword(s):  
Calculation Method ◽  
Image Watermarking ◽  
Robust Watermarking ◽  
Exponential Transform ◽  
Computational Simplicity ◽  
Polar Complex Exponential Transform ◽  
Watermarking Scheme ◽  
Complex Exponential ◽  
Grid Division ◽  
Geometrical Attacks

Generic polar complex exponential transform (GPCET), as continuous orthogonal moment, has the advantages of computational simplicity, numerical stability, and resistance to geometric transforms, which make it suitable for watermarking. However, errors in kernel function discretization can degrade these advantages. To maximize the GPCET utilization in robust watermarking, this paper proposes a secondary grid-division (SGD)-based moment calculation method that divides each grid corresponding to one pixel into nonoverlapping subgrids and increases the number of sampling points. Using the accurate moment calculation method, a nonsubsampled contourlet transform (NSCT)–GPCET-based watermarking scheme with resistance to image processing and geometrical attacks is proposed. In this scheme, the accurate moment calculation can reduce the numerical error and geometrical error of the traditional methods, which is verified by an image reconstruction comparison. Additionally, NSCT and accurate GPCET are utilized to achieve watermark stability. Subsequent experiments test the proposed watermarking scheme for its invisibility and robustness, and verify that the robustness of the proposed scheme outperforms that of other schemes when its level of invisibility is significantly higher.

Secured Medical Image Hashing Based on Frequency Domain with Chaotic Map

2021 ◽  
Vol 39 (5A) ◽  
pp. 711-722
Author(s):  
Amira K. Jabbar ◽  
Ashwaq T. Hashim ◽  
Qusay F. Al-Doori
Keyword(s):  
Medical Image ◽  
Personal Information ◽  
Chaotic Map ◽  
Region Of Interest ◽  
Image Hashing ◽  
Medical Field ◽  
Hash Algorithm ◽  
Frequency Domains ◽  
Hash Code ◽  
Shift Keying

Recently, online-medicine got increased global interest, particularly during COVID19 pandemic. Data protection is important in the medical field since when promoting telemedicine applications, it is necessary to protect the patient data and personal information. A secured process is needed to transmit medical images over the Internet. In this paper hash algorithm is employed to protect the data by using powerful features from the coupled frequency domains of the Slantlet Transformation (SLT) and the Discrete Cosine Transform (DCT). The Region of Interest (ROI) is localized from an MRI image then extraction of a feature set is performed for calculating the hash code. Then, hash code is enciphered to maintain security by employing a secure Chaotic Shift Keying (CSK). The suggested method of security is ensured by the strength of the CSK and the encryption key secrecy.  A detailed analysis was conducted using 1000 uncompressed images that were chosen randomly from a publicly available AANLIB database. The proposed methodology can be useful for JPEG compression. Also, this method could resist many attacks of image processing likes filtering, noise addition, and some geometric transforms.

Sign in / Sign up

Export Citation Format

Share Document