scholarly journals Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

2011 ◽  
pp. 265-270 ◽  
Author(s):  
Raúl Alonso-Calvo ◽  
Jose Crespo ◽  
Victor Maojo ◽  
Alberto Muñoz ◽  
Miguel García-Remesal ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Medical Image ◽  
Image Data ◽  
Data Sets ◽  
Cloud Computing Service ◽  
Medical Image Data

scholarly journals Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine

2017 ◽  
Vol 3 (2) ◽  
pp. 207-210
Author(s):  
Manuel Stich ◽  
Jeannine Vogt ◽  
Michaela Lindner ◽  
Ralf Ringler
Keyword(s):  
Personalized Medicine ◽  
Multimodal Imaging ◽  
Medical Image ◽  
Current Trend ◽  
Image Data ◽  
Surrounding Tissue ◽  
Artificial Noise ◽  
Data Sets ◽  
Segmentation Methods ◽  
Medical Image Data

AbstractMultimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation (< 3%) can be achieved with methods of machine learning, multithreshold and advanced level-set method - even at high artificial noise (SNR< 18). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.

Integration Method for Information System with Cloud Computing

2011 ◽  
Vol 368-373 ◽  
pp. 3473-3476 ◽  
Author(s):  
Jie Liu ◽  
Xian Sheng Qin
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Information Integration ◽  
Medical Image ◽  
Integration Method ◽  
Image Data ◽  
Medical Data ◽  
Application Layer ◽  
Mapreduce Framework ◽  
Medical Image Data

This work proposes a method of information integration based Cloud Computing. Users can ask for services through application layer, using the open source Hadoop and implementing medical image data storage and analysis. The functional level of this system is on the basis of service. In our experiments, using the MapReduce framework, efficiently implement the DCM format medical data convert to JPEG format picture. We are working on the function to directly read the medical data which is stored in PACS.

scholarly journals Secure eHealth: A Secure eHealth System to Detect COVID using Image Steganography

2020 ◽  
Author(s):  
Nandhini Subramanian ◽  
Omar Elharrouss ◽  
Somaya Al-Maadeed
Keyword(s):  
Cloud Computing ◽  
Medical Image ◽  
Image Data ◽  
Healthcare Systems ◽  
Image Steganography ◽  
Privacy And Security ◽  
The World ◽  
Medical Image Data

COVID-19 is a pandemic which has spread to all parts of the world. Detection of COVID infection is crucial to prevent the spread further. Contactless healthcare systems are essential which can be implemented with Cloud computing. Privacy and security of the medical image data transferred through untrusted channels cannot be ensured. The main aim is to secure the medical details when transferring them from the end device to the cloud and vice versa using image steganography. The medical lung images are masked under a normal and natural cover 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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