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Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.

Shunxing Bao¹, Stephen M Damon², Bennett A Landman³

¹Computer Science, Vanderbilt University, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering

|April 30, 2016

Summary

This study integrates the Java Image Science Toolkit (JIST) with Amazon Web Services (AWS) cloud computing for medical image processing. The framework offers an affordable, scalable solution, overcoming memory limitations and simplifying cloud adoption for researchers.

Keywords:

Amazon Web Service Cloud deployment High performance computing NITRC-CE

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Area of Science:

Medical Image Processing
Cloud Computing
High-Performance Computing

Background:

Medical image processing demands significant computational resources, often exceeding local capabilities.
The Java Image Science Toolkit (JIST) provides a user-friendly pipeline for medical image analysis but can face memory bottlenecks.
Amazon Web Services (AWS) offers scalable and cost-effective cloud solutions for demanding computational tasks.

Purpose of the Study:

To develop an affordable, scalable, and user-friendly AWS framework for JIST.
To enable efficient medical image processing in the cloud without requiring extensive Java programming knowledge.
To address memory limitations and enhance the accessibility of advanced image analysis pipelines.

Main Methods:

Integration of JIST with AWS cloud computing services, including EC2 instances and S3 storage.
Development of a unified control panel for configuring cloud resources (nodes, memory, processors).
Implementation of a cost-benefit analysis comparing local execution with cloud-based processing.

Main Results:

Successful integration of JIST with AWS, creating a scalable medical image processing framework.
Demonstrated alleviation of memory bottlenecks through cloud resource utilization.
Provided a cost-benefit analysis to guide researchers in choosing between local and cloud execution.

Conclusions:

The developed AWS framework enhances JIST's capabilities for large-scale medical image processing.
This approach makes advanced image analysis more accessible and cost-effective for researchers.
Cloud integration offers a viable solution for overcoming computational limitations in medical imaging research.