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Performance comparison of compression algorithms for archiving segmented volumetric binary medical data.

Felix Fischer¹, M Alper Selver², Oguz Dicle³

¹Nautavis GmbH, Linnich, Aachen, GERMANY.

Studies in Health Technology and Informatics

|August 28, 2014

Summary

This summary is machine-generated.

Archiving medical image segmentation results requires efficient data compression. This study found the JBIG2 compression method offers the best performance for lossless archiving of segmented volumes.

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Area of Science:

Medical Imaging
Data Compression
Computational Pathology

Background:

Archiving segmentation results is crucial for longitudinal studies and data reproducibility.
Binary segmentation data often requires significant storage, necessitating efficient compression techniques.
Lossless compression is essential to preserve the integrity of segmentation data for accurate restoration.

Purpose of the Study:

To evaluate various lossless compression methods for archiving binary segmentation results.
To assess the suitability of different compression approaches for restoring segmented volumes.
To identify the optimal compression technique balancing compression ratio and processing time.

Main Methods:

Tested multiple compression algorithms on diverse clinical segmentation datasets.
Evaluated compression performance based on compression ratio and processing speed.
Assessed the fidelity of restored segmentation data after decompression.

Main Results:

The JBIG2 compression method demonstrated superior performance across tested datasets.
JBIG2 achieved the highest compression ratios compared to other methods.
JBIG2 also exhibited favorable processing times for compression and decompression.

Conclusions:

JBIG2 is the recommended method for lossless compression of binary segmentation data.
Efficient archiving of segmentation results can be achieved using JBIG2.
This facilitates better data management in medical imaging research and clinical practice.