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An Efficient Middle Layer Platform for Medical Imaging Archives
1Department of Computer Engineering, Kırıkkale University, 71450 Kırıkkale, Turkey.
Journal of Healthcare Engineering
|July 24, 2018
Summary
This study introduces an efficient platform for medical image big data archiving. It significantly reduces processing time and achieves high compression ratios, offering a scalable cloud solution.
Area of Science:
- Medical Imaging
- Big Data Analytics
- Cloud Computing
Background:
- Digital medical images are crucial for diagnosis and treatment.
- Growing data volumes and complex formats pose significant archiving challenges for traditional systems.
- Existing systems struggle to process and store large medical image datasets effectively.
Purpose of the Study:
- To develop an efficient middle layer platform for medical image big data compression and archiving.
- To leverage Hadoop and MongoDB architecture for scalable data management.
- To improve existing medical image compression techniques.
Main Methods:
- Developed a scalable platform using Hadoop's MapReduce programming model.
- Integrated MongoDB (NoSQL database) for enhanced performance.
- Built and evaluated a four-node Hadoop cluster using actual patient medical images.
Main Results:
- Reduced image processing time from 15,599 seconds on a single node to 8,153 seconds on the developed platform.
- Achieved high compression ratios for non-Region of Interest (ROI) images, ranging from 92.12% to 97.84%.
- Demonstrated the platform's scalability and efficiency.
Conclusions:
- The proposed platform offers an integrated, cloud-based solution for medical image archiving.
- The system effectively addresses the challenges of big data in medical imaging.
- This approach enhances data preservation, protection, and accessibility for healthcare.
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