Parallel disk arrays provide an architecture for high-performance acquisition and archival storage for medical imaging

1992 ◽  
Author(s):  
Ronald J. Clouthier
Keyword(s):  
Medical Imaging ◽  
High Performance ◽  
Disk Arrays ◽  
Parallel Disk ◽  
Archival Storage

scholarly journals Ultrasound B-Mode Imaging: Beamforming and Image Formation Techniques

2019 ◽  
Vol 9 (12) ◽  
pp. 2507
Author(s):  
Giulia Matrone ◽  
Alessandro Ramalli ◽  
Piero Tortoli
Keyword(s):  
Medical Imaging ◽  
High Performance ◽  
Image Formation ◽  
High Performance Systems ◽  
Active Research

In the last decade, very active research in the field of ultrasound medical imaging has brought to the development of new advanced image formation techniques and of high-performance systems able to effectively implement them [...]

The Design of SkyPACS: A High-Performance Mobile Medical Imaging Solution

2014 ◽  
pp. 119-133
Author(s):  
Tananan Pattanangkur ◽  
Sikana Tanupabrungson ◽  
Katchaguy Areekijseree ◽  
Sarunya Pumma ◽  
Tiranee Achalakul
Keyword(s):  
Medical Imaging ◽  
High Performance

Object-oriented framework for high-performance electronic medical imaging

1996 ◽  
Author(s):  
Douglas C. Schmidt ◽  
Timothy H. Harrison ◽  
Irfan Pyarali
Keyword(s):  
Medical Imaging ◽  
High Performance ◽  
Object Oriented

scholarly journals Referential DNA Data Compression using Hadoop Map Reduce Framework

2019 ◽  
Vol 17 (2) ◽  
pp. 207-214
Author(s):  
Raju Bhukya ◽  
Sumit Deshmuk
Keyword(s):  
Data Compression ◽  
Dna Sequences ◽  
High Performance ◽  
Deoxyribonucleic Acid ◽  
Genetic Data ◽  
Disk Arrays ◽  
Distributed Environment ◽  
Mapreduce Framework ◽  
Hadoop Mapreduce ◽  
Time Required

The indispensable knowledge of Deoxyribonucleic Acid (DNA) sequences and sharply reducing cost of the DNA sequencing techniques has attracted numerous researchers in the field of Genetics. These sequences are getting available at an exponential rate leading to the bulging size of molecular biology databases making large disk arrays and compute clusters inevitable for analysis.In this paper, we proposed referential DNA data compression using hadoop MapReduce Framework to process humongous amount of genetic data in distributed environment on high performance compute clusters. Our method has successfully achieved a better balance between compression ratio and the amount of time required for DNA data compression as compared to other Referential DNA Data Compression methods.

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