scholarly journals A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

2020 ◽  
Vol 9 (3) ◽  
pp. 3418-3427
Keyword(s):  
Image Encryption ◽  
Medical Image ◽  
Parallel Implementation ◽  
Computation Time ◽  
Image Data ◽  
Processing Technique ◽  
Initial Image ◽  
Result Section ◽  
Modulo Operation ◽  
Group Modulo

Image encryption is a technique that provides security to an image and their data from unauthorized access in which there is the lightweight process (LWP) that can be parallelized resulting in the reduction of computation time. In this paper, parallel lossless image encryption, as well as the decryption technique, is proposed. The method is a parallel implementation of group modulo operation (GMO) based bit circular shift (BCS) of pixel bit-plane values. The backbone of this technique is circular bit rotation based on some modulo group key value. The key value used here is the result of group modulo operation. The binary bit values of pixels of the initial Image are rotated circularly to generate a new binary bit value of pixels encrypted image. The enhancement of this GMO and BCS based encryption are also given here by using the parallel implementation of the algorithm. The given results show the parallel implementation technique has the same level of encryption standard but has a better level of the time standard. As discussed in the result section, this technique can be used for medical image encryption as well as in multimedia applications where the transfer of image data is required over a network.

scholarly journals Randomized Key-based GMO-BCS Image Encryption for Securing Medical Image

2019 ◽  
Vol 8 (3) ◽  
pp. 1716-1722 ◽  
Keyword(s):  
Image Encryption ◽  
Digital Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Security Requirement ◽  
Direct Transmission ◽  
Dicom Image ◽  
Security Enhancement ◽  
Image Encryption Algorithm ◽  
Group Modulo

An essential security requirement while transmitting and receiving medical images is to maintain confidentiality and authorization of these medical images. This paper contains a proposal of an enhanced lossless image encryption algorithm that provides security to Digital Imaging and Communications in Medicine (DICOM) images by producing a random key with using enhanced group modulo based bit circular shift (GMO-BCS) technique. Random key production is the backbone of this technique to provide robust security of medical images that transfer over a network. In the encryption process, we randomly generate a key for each and every pixel of the DICOM image. Group theory is used in this process to create circular shifting in 8-bit pixel values while the security enhancement employs the random key for encryption. This technique is more suitable for medical image encryption either by direct transmission or multimedia app-based transmission under telemedicine and others

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS

1995 ◽  
Vol 09 (04) ◽  
pp. 615-634 ◽  
Author(s):  
VIRGINIE MARION-POTY ◽  
SERGE MIGUET
Keyword(s):  
Parallel Implementation ◽  
Image Data ◽  
Data Allocation ◽  
Load Imbalance ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Recursive Operators ◽  
Allocation Strategies

This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the case of recursive operators, they lead to nearly sequential executions. In this framework, we show the usefulness of block-cyclic allocations. Finally, we illustrate the fact that allocating the same amount of image data to each processor can lead to severe load imbalance in the case of some operators with data-dependant execution times.

scholarly journals Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

2021 ◽  
Vol 14 ◽  
Author(s):  
Eric Nathan Carver ◽  
Zhenzhen Dai ◽  
Evan Liang ◽  
James Snyder ◽  
Ning Wen
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Model Performance ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Generative Adversarial Network ◽  
Post Contrast ◽  
Adversarial Network ◽  
Fluid Attenuated Inversion Recovery

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. MRI plays an essential role in the diagnosis and treatment assessment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigated the creation of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (Flair) MR images. These synthetic MR (synMR) images were assessed quantitatively with four metrics. The synMR images were also assessed qualitatively by an authoring physician with notions that synMR possessed realism in its portrayal of structural boundaries but struggled to accurately depict tumor heterogeneity. Additionally, this study investigated the synMR images created by generative adversarial network (GAN) to overcome the lack of annotated medical image data in training U-Nets to segment enhancing tumor, whole tumor, and tumor core regions on gliomas. Multiple two-dimensional (2D) U-Nets were trained with original BraTS data and differing subsets of the synMR images. Dice similarity coefficient (DSC) was used as the loss function during training as well a quantitative metric. Additionally, Hausdorff Distance 95% CI (HD) was used to judge the quality of the contours created by these U-Nets. The model performance was improved in both DSC and HD when incorporating synMR in the training set. In summary, this study showed the ability to generate high quality Flair, T2, T1, and T1CE synMR images using GAN. Using synMR images showed encouraging results to improve the U-Net segmentation performance and shows potential to address the scarcity of annotated medical images.

scholarly journals Design and Implementation of Compressed Medical Data over an Authenticated Secured

2020 ◽  
Vol 9 (3) ◽  
pp. 1543-1548
Keyword(s):  
Medical Image ◽  
Research Work ◽  
Health Sector ◽  
Image Data ◽  
Medical Data ◽  
Information Network ◽  
Design And Implementation ◽  
Simulation Results ◽  
Medical Image Data ◽  
Secure Transmission

Ideally, secure transmission of medical image data is one of the major challenges in health sector. The National Health Information Network has to protect the data in confidential manner. Storage is also one of the basic concern along with secure transmission. In this paper we propose an algorithm that supports confidentiality, authentication and integrity implementation of the scrambled data before transmitting on the communication medium. Before communication the data is compressed while keeping data encrypted. The research work demonstrate with simulation results. The results shows that the proposed work effectively maintains confidentiality, authentication and integrity. The experimental results evaluated medical image quality like PSNR, MSE, SC, and NAEetc.

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