Regional medical image storage system based on Hadoop

Abstract

The invention relates to a regional medical image storage system based on Hadoop. The regional medical image storage system comprises a management system, a commercial cloud storage system and a private cloud storage system, wherein the user management in the management system is mainly used for a manager to manage a user of the system and to strictly mange the operation right of the user on the data; the data management is mainly used for managing the data in the cloud storage system; the commercial cloud storage system is built by an HDFS ((Hadoop Distributed File System) distribution file system of the Hadoop, and is used for storing image data which is not frequently used in a hospital; the private cloud storage system consists of high-performance PCs (Personnel Computers) in each department, and performs data interaction through optical fiber; and the private cloud storage system comprises an image collection work station, a diagnosis server and a printing server of each department. Through the regional medical image storage system, medical image remote group consultation, image transfer treatment, virtual image specialist service, remote teaching and image content searching service can be performed; and the sufficient sharing and the efficient cooperation of image equipment in the region and the image diagnosis technology are realized.

Description

technical field [0001] The invention belongs to the technical field of digital medical information, and specifically relates to a Hadoop-based distributed regional medical image storage system and a medical image storage format. Background technique [0002] In recent years, with the continuous development of social economy, medical imaging information technology has been widely used in hospitals at all levels and types, and referring to medical images has already become an important means for doctors to diagnose; followed by a large number of medical images every day data, and there is an ever-increasing trend. At present, the scale of PACS images processed by an ordinary tertiary hospital has reached TB to dozens of TB levels. Therefore, how to manage these huge medical data more effectively is a difficult problem for medical workers. . [0003] At present, major hospitals mainly use PACS (Picture Archiving and Communication Systems) system to manage various medical image...

AI Technical Summary

Problems solved by technology

[0012] 1. The common image size in medicine is about 512KB, such as CT, X-ray film, etc. Generally, the number of images taken at one time is about 5MB, which is much smaller than the default 64MB data block space in Hadoop , once a large number of such small files are stored in the HDFS file system, it is bound to bring a great burden to the NameNode node in HDFS due to excessive metadata, resulting in a decrease in the performance of the entire cluster

[0013] 2. In clinical medicine, the real-time requirements for medical image data are very high, and when writing data in the HDFS system, three data copies must be created for each data block, resulting in poor writing ability, so Hadoop The reading and writing performance is not suitable for clinical applications with high real-time requirements

[0014] In view of the characteristics of Hadoop mentioned above, although it has advantages in storage and computing, the real-time nature of data is its disadvantage.

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