SP-MIOV: A novel framework of shadow proxy based medical image online visualization in computing and storage resource restrained environments

2020 ◽  
Vol 105 ◽  
pp. 318-330 ◽  
Author(s):  
Wei Li ◽  
Kun Yu ◽  
Chaolu Feng ◽  
Dazhe Zhao
Keyword(s):  
Medical Image ◽  
Storage Resource ◽  
And Storage

Enhancing the Security of Exchanging and Storing DICOM Medical Images on the Cloud

2018 ◽  
Vol 8 (1) ◽  
pp. 154-172 ◽  
Author(s):  
O. Dorgham ◽  
Banan Al-Rahamneh ◽  
Ammar Almomani ◽  
Moh'd Al-Hadidi ◽  
Khalaf F. Khatatneh
Keyword(s):  
Digital Signature ◽  
Medical Image ◽  
Medical Images ◽  
Software Implementation ◽  
Symmetric Encryption ◽  
Hybrid Encryption ◽  
Encryption Algorithms ◽  
Asymmetric Encryption ◽  
And Storage ◽  
Image Format

Medical image information can be exchanged remotely through cloud-based medical imaging services. Digital Imaging and Communication in Medicine (DICOM) is considered to be the most commonly used medical image format among hospitals. The objective of this article is to enhance the secure transfer and storage of medical images on the cloud by using hybrid encryption algorithms, which are a combination of symmetric encryption algorithms and asymmetric encryption algorithms that make the encryption process faster and more secure. To this end, three different algorithms are chosen to build the framework. These algorithms are simple and suitable for hardware or software implementation because they require low memory and low computational power yet provide high security. Also, security was increased by using a digital signature technique. The results of the analyses showed that for a DICOM file with size 12.5 Mb, 2.957 minutes was required to complete the process. This was totaled from the encryption process took 1.898 minutes, and the decryption process took 1.059 minutes.

scholarly journals Biobtree: A tool to search and map bioinformatics identifiers and special keywords

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 145
Author(s):  
Tamer Gur
Keyword(s):  
Web Services ◽  
Open Source ◽  
Resource Usage ◽  
Web Interface ◽  
Bioinformatics Tool ◽  
Link Type ◽  
As Species ◽  
Storage Resource ◽  
Executable File ◽  
And Storage

Biobtree is a bioinformatics tool to search and map bioinformatics datasets via identifiers or special keywords such as species name. It processes large bioinformatics datasets using a specialized MapReduce-based solution with optimum computational and storage resource usage. It provides uniform and B+ tree-based database output, a web interface, web services and allows performing chain mapping queries between datasets. It can be used via a single executable file or alternatively it can be used via the R or Python-based wrapper packages which are additionally provided for easier integration into existing pipelines. Biobtree is open source and available at GitHub.

scholarly journals An Efficient Lossless Compression algorithm for Medical images

2021 ◽  
Vol 12 (3) ◽  
pp. 3793-3799
Author(s):  
Amanpreet Kaur Sandhu
Keyword(s):  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Grayscale Image ◽  
Blocking Artifacts ◽  
Frequency Model ◽  
Medical Image Compression ◽  
Block Boundary ◽  
Ringing Artifacts ◽  
And Storage

Medical image compression plays a vital role in diagnosis of diseases which allowing manipulation, efficient, transmission and storage of color, binary and grayscale image. Before transmission and storage, a medical image may be required to be compressed. The objective of the study is to develop an efficient and effective technique for digital medical images which alleviates the blocking artifacts from grayscale image while retaining all relevant structures. In this paper, we demonstrate a highly engineered postprocessing filtering approach has been designed to remove blocking effects from medical images at low bit rate. The proposed technique is comprised of three strategies i.e. 1) a threshold valve scheme which is used to capture the pixel vectors containing blocking artifacts. 2) Blocking artifacts measurement techniques. The blocking artifacts are measured by three frequency related modes (low, Moderate and high frequency model). 3)  A directional filter which is used to remove over-smoothing and ringing artifacts near edges of block boundary. The algorithm is tested on digital medical grayscale images from different modalities. The experimental results illustrate that the proposed technique is more efficient on the basis of PSNR-B, MSSIM, and MOS indices than the state-of-the-art methods. The proposed algorithm can be seamlessly applied in area of medical image compression which high transmission efficiency and acceptable image quality can be guaranteed.

Rapid Extraction and Compression of DICOM Data for Medical Image Geometric Modeling

2012 ◽  
Vol 433-440 ◽  
pp. 7511-7515
Author(s):  
Xue Mei Huang ◽  
Jin Chuan Wang
Keyword(s):  
Geometric Modeling ◽  
Medical Image ◽  
Transmission Efficiency ◽  
Memory Space ◽  
Block Coding ◽  
Rapid Extraction ◽  
Run Length Coding ◽  
And Storage ◽  
Compressed Data ◽  
Single Pixel

This paper presents a method of extracting and compressing required data from the DCM file for medical image geometric modeling. According to the characteristics of DICOM data, combining the idea of run-length coding with block coding, the rapid data compression and storage in RAM was realized finally. Compared with other coding methods, the encoding approach for DICOM data in this paper, not only saves the memory space and improves transmission efficiency, but also can read the required a single pixel, or part of pixel data from the compressed data conveniently.

3D Medical Images Compression

2021 ◽  
pp. 1046-1067
Author(s):  
Mohamed Fawzy Aly ◽  
Mahmood A. Mahmood
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Medical Data ◽  
The Body ◽  
Medical Image Compression ◽  
Diagnostic Data ◽  
Digital Representations ◽  
And Storage

Medical images are digital representations of the body. Medical imaging technology has improved tremendously in the past few decades. The amount of diagnostic data produced in a medical image is vast and as a result could create problems when sending the medical data through a network. To overcome this, there is a great need for the compression of medical images for communication and storage purposes. This chapter contains an introduction to compression types, an overview of medical image modalities, and a survey on coding techniques that deal with 3D medical image compression.

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