Medical Data Compression and Sharing Technology Based on Blockchain

2020 ◽  
pp. 581-592
Author(s):  
Yi Du ◽  
Hua Yu
Keyword(s):  
Data Compression ◽  
Medical Data

A fast and efficient algorithm for volumetric medical data compression and retrieval

2007 ◽  
Author(s):  
Linning Ye ◽  
Jiangling Guo ◽  
Sunanda Mitra ◽  
Brian Nutter
Keyword(s):  
Data Compression ◽  
Efficient Algorithm ◽  
Medical Data

Medical Data Compression for Lossless Data Transmission and Archival

2021 ◽  
pp. 55-74
Author(s):  
Ramesh Sekaran ◽  
Vimal Kumar Maaanuguru Nagaraju ◽  
Vijayalakshmi Jagadeesan ◽  
Manikandan Ramachandran ◽  
Ambeshwar Kumar
Keyword(s):  
Data Compression ◽  
Data Transmission ◽  
Medical Data

scholarly journals Medical Data Compression and Transmission in Noisy WLANS: A Review

2019 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Mustafa Almahdi Algaet ◽  
Abd Samad Bin Hasan Basari ◽  
Ali Ahmad Milad ◽  
Salem Msaoud Adrugi ◽  
Salem Mustafa Aldeep
Keyword(s):  
Data Compression ◽  
Medical Data

scholarly journals LSPIHT Algorithm for ECG Data Compression and Transmission for Telemedicine Applications

2012 ◽  
Vol 60 (1) ◽  
pp. 31-36
Author(s):  
Tahmina Zebin ◽  
Ekramul Farook ◽  
Syeda Zinath Aman ◽  
Shahida Rafique
Keyword(s):  
Data Compression ◽  
Medical Image Analysis ◽  
Rate Distortion ◽  
Healthcare Services ◽  
Medical Data ◽  
Set Partitioning ◽  
Telecommunication Networks ◽  
Base Layer ◽  
Ecg Data Compression ◽  
Ecg Data

Medical image analysis and data compression are rapidly evolving fields with growing applications in healthcare services e.g. teleradiology, teleconsultation, e-health, telemedicine and statistical analysis of medical data. In this paper, a Layered Set Partitioning in Hierarchical Trees (LSPIHT) algorithm for medical data compression and transmission is presented. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, medical data can be reconstructed with various signal-to-noise ratios (SNRs) and resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. The algorithm is compared with other algorithms for encoding ECG data, and analysis shows that the LSPIHT attains better rate-distortion performance and low network complexity than other encoding techniques.DOI: http://dx.doi.org/10.3329/dujs.v60i1.10332  Dhaka Univ. J. Sci. 60(1): 31-36, 2012 (January)

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