MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM)

Omniah Nagoor, Joss Whittle, Jingjing Deng, Benjamin Mora and Mark W. Jones

Abstract

As scanners produce higher-resolution and more densely sampled images, this raises the challenge of data storage, transmission and communication within healthcare systems. Since the quality of medical images plays a crucial role in diagnosis accuracy, medical imaging compression techniques are desired to reduce scan bitrate while guaranteeing lossless reconstruction. This paper presents a lossless compression method that integrates a Recurrent Neural Network (RNN) as a 3D sequence prediction model. The aim is to learn the long dependencies of the voxel's neighbourhood in 3D using Long Short-Term Memory (LSTM) network then compress the residual error using arithmetic coding. Experiential results reveal that our method obtains a higher compression ratio achieving 15% saving compared to the state-of-the-art lossless compression standards, including JPEG-LS, JPEG2000, JP3D, HEVC, and PPMd. Our evaluation demonstrates that the proposed method generalizes well to unseen modalities CT and MRI for the lossless compression scheme. To the best of our knowledge, this is the first lossless compression method that uses LSTM neural network for 16-bit volumetric medical image compression.

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DOI

10.1109/ICPR48806.2021.9413341
https://dx.doi.org/10.1109/ICPR48806.2021.9413341

Citation

Omniah Nagoor, Joss Whittle, Jingjing Deng, Benjamin Mora and Mark W. Jones, MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM), 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 2874-2881. https://doi.org/10.1109/ICPR48806.2021.9413341

Bibtex

@inproceedings{MedZip, 
author={Omniah Nagoor and Joss Whittle and Jingjing Deng and Benjamin Mora and Mark W. Jones}, 
booktitle={2020 25th International Conference on Pattern Recognition (ICPR)}, 
title={MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM)}, 
year={2021},
pages={2874-2881},
doi={10.1109/ICPR48806.2021.9413341},
}