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Related Experiment Video

Updated: Dec 27, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine.

Vinicius Facco Rodrigues1, Euclides Palma Paim1, Rafael Kunst1

  • 1Universidade do Vale do Rio dos Sinos, Av. Unisinos, 950, São Leopoldo, RS, Brazil.

Computer Methods and Programs in Biomedicine
|February 29, 2020
PubMed
Summary

PS2DICOM enhances real-time medical image sharing using multilevel cloud elasticity and adaptive compression, improving Digital Imaging and Communications in Medicine (DICOM) data transmission efficiency by 35%. This facilitates seamless remote collaboration among specialists.

Keywords:
Cloud computingCompressionDICOMPublish/subscribeTelemedicine

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Area of Science:

  • Cloud Computing
  • Medical Imaging
  • Telemedicine

Background:

  • Medical specialties depend on Digital Imaging and Communications in Medicine (DICOM) for telemedicine diagnosis.
  • Real-time, simultaneous transmission of DICOM images to multiple physicians presents significant storage and bandwidth challenges.
  • Existing cloud solutions for DICOM data lack optimization strategies leveraging cloud elasticity.

Purpose of the Study:

  • To propose PS2DICOM, a cloud-based publish/subscribe (PubSub) model designed to enhance DICOM data transmission performance.
  • To implement multilevel resource elasticity within the cloud infrastructure to improve service availability and performance.
  • To reduce network resource demand through adaptive data compression techniques.

Main Methods:

  • Developed a prototype PS2DICOM system utilizing a PubSub communication model with distinct publisher and subscriber roles.
  • Implemented two levels of cloud elasticity: resource scaling for brokers and data storage.
  • Integrated DEFLATE, LZMA, and BZIP2 compression algorithms with dynamic client-side compression level adjustment based on network throughput.

Main Results:

  • PS2DICOM demonstrated improvements in DICOM image transmission quality, storage, querying, and retrieval.
  • Achieved an approximate 35% overall efficiency gain in data sending and receiving operations.
  • Transparent, automatic scaling of resources via multilevel elasticity contributed to the observed efficiency improvements.

Conclusions:

  • PS2DICOM effectively utilizes multilevel cloud elasticity and adaptive data compression to optimize DICOM data transmission.
  • The system enhances real-time communication for geographically dispersed medical specialists.
  • PS2DICOM offers a viable solution for improving the efficiency and quality of remote medical image analysis.