[Comparative Analysis of Three Massive Medical Image Storage Technologies]

Area of Science:

• Medical Informatics
• Data Storage Technologies
• Health Information Management

Background:

• Massive medical data generation necessitates advanced storage solutions beyond traditional local systems.
• Increasing use of cloud services for medical data sharing demands higher data transmission speeds.
• Existing local storage systems face challenges with speed, visualization, and cost-effectiveness for big data.

Purpose of the Study:

• To evaluate and compare the transmission speed of mainstream storage technologies for medical images.
• To identify optimal storage systems for handling the demands of medical image big data.
• To analyze storage options based on factors like high availability and cost.

Main Methods:

• Conducted transmission speed tests on three mainstream storage technologies for medical images.
• Evaluated performance metrics including access speed and system efficiency.
• Assessed storage solutions considering high availability and economic factors.

Main Results:

• Object storage systems demonstrated superior access speed and performance compared to traditional local storage systems for medical image big data.
• Analysis considered multiple aspects including high availability and cost-effectiveness.
• While object storage showed performance benefits, a comprehensive recommendation was made.

Conclusions:

• Distributed file storage systems, such as HDFS (Hadoop Distributed File System), are recommended as the primary choice for medical image big data storage.
• Object storage systems offer advantages in access speed for medical image big data.
• The study provides insights for selecting appropriate storage infrastructure for evolving healthcare data needs.