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.

Similarity Searching of Medical Image Data in Distributed Systems

2012 ◽  
pp. 58-77
Author(s):  
Amalia Charisi ◽  
Panagiotis Korvesis ◽  
Vasileios Megalooikonomou
Keyword(s):  
Distributed Systems ◽  
Image Retrieval ◽  
Medical Image ◽  
Image Data ◽  
Medical Data ◽  
Similarity Searching ◽  
Network Bandwidth ◽  
Local Site ◽  
Distributed Index ◽  
Medical Image Data

In this paper, the authors propose a method for medical image retrieval in distributed systems to facilitate telemedicine. The proposed framework can be used by a network of healthcare centers, where some can be remotely located, assisting in diagnosis without the necessary transfer of patients. Security and confidentiality issues of medical data are expected, which are handled at the local site following the procedures and protocols of each institution. To make the search more effective, the authors introduce a distributed index based on features that are extracted from each image. Considering network bandwidth limitations and other restrictions that are associated with handling medical data, the images are processed locally and a pointer is distributed in the network. For the distribution of this pointer, the authors propose a function that maps the pointer of each image to a node with similar contents.

scholarly journals Consistency of Medical Data Using Intelligent Neuron Faster R-CNN Algorithm for Smart Health Care Application

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 185
Author(s):  
Seong-Kyu Kim ◽  
Jun-Ho Huh
Keyword(s):  
Medical Image ◽  
High Performance ◽  
Human Life ◽  
Intelligent Agent ◽  
Image Data ◽  
Modern Society ◽  
Original Data ◽  
Medical Data ◽  
Cloud Architecture ◽  
Medical Image Data

The purpose of this study is to increase interest in health as human life is extended in modern society. Hence, many people in hospitals produce much medical data (EMR, PACS, OCS, EHR, MRI, X-ray) after treatment. Medical data are stored as structured and unstructured data. However, many medical data are causing errors, omissions and mistakes in the process of reading. This behavior is very important in dealing with human life and sometimes leads to medical accidents due to physician errors. Therefore, this research is conducted through the CNN intelligent agent cloud architecture to verify errors in reading existing medical image data. To reduce the error rule when reading medical image data, a faster R-CNN intelligent agent cloud architecture is proposed. It shows the result of increasing errors of existing error reading by more than 1.4 times (140%). In particular, it is an algorithm that analyses data stored by actual existing medical data through Conv feature map using deep ConvNet and ROI Projection. The data were verified using about 120,000 databases. It uses data to examine human lungs. In addition, the experimental environment established an environment that can handle GPU’s high performance and NVIDIA SLI multi-OS and multiple Quadro GPUs were used. In this experiment, the verification data composition was verified and randomly extracted from about 120,000 medical records and the similarity compared to the original data were measured by comparing about 40% of the extracted images. Finally, we want to reduce and verify the error rate of medical data reading.

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.

Medical Data Storage and Compression

2019 ◽  
pp. 140-154
Author(s):  
Saravanan Chandran
Keyword(s):  
Data Storage ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Data ◽  
Basic Structure ◽  
Medical Data ◽  
Dicom Image ◽  
Data Formats ◽  
History Of ◽  
Medical Image Data

This chapter introduces medical imaging devices and its history. Further, the chapter discusses the history of the DICOM image and DICOM standard. The chapter also discusses image acquisition. Moreover, the chapter discusses the various software used for processing DICOM image. The chapter also discusses the limitations of DICOM and other medical image data formats. The basic structure of the DICOM is described in this chapter. Further, various research articles on medical image processing are discussed.

Similarity Searching of Medical Image Data in Distributed Systems

2011 ◽  
Vol 2 (1) ◽  
pp. 60-79
Author(s):  
Amalia Charisi ◽  
Panagiotis Korvesis ◽  
Vasileios Megalooikonomou
Keyword(s):  
Distributed Systems ◽  
Image Retrieval ◽  
Medical Image ◽  
Image Data ◽  
Medical Data ◽  
Similarity Searching ◽  
Network Bandwidth ◽  
Local Site ◽  
Distributed Index ◽  
Medical Image Data

In this paper, the authors propose a method for medical image retrieval in distributed systems to facilitate telemedicine. The proposed framework can be used by a network of healthcare centers, where some can be remotely located, assisting in diagnosis without the necessary transfer of patients. Security and confidentiality issues of medical data are expected, which are handled at the local site following the procedures and protocols of each institution. To make the search more effective, the authors introduce a distributed index based on features that are extracted from each image. Considering network bandwidth limitations and other restrictions that are associated with handling medical data, the images are processed locally and a pointer is distributed in the network. For the distribution of this pointer, the authors propose a function that maps the pointer of each image to a node with similar contents.

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