Conference Paper/Proceeding/Abstract 909 views 311 downloads
Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
,
Mark Jones
2020 IEEE International Conference on Image Processing (ICIP), Pages: 2815 - 2819
Swansea University Authors:
Omniah Nagoor, Jingjing Deng, Benjamin Mora , Mark Jones
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DOI (Published version): 10.1109/icip40778.2020.9191031
Abstract
Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless com...
| Published in: | 2020 IEEE International Conference on Image Processing (ICIP) |
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| ISBN: | 978-1-7281-6396-3 9781728163956 |
| ISSN: | 1522-4880 2381-8549 |
| Published: |
IEEE
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa55355 |
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| Abstract: |
Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless compression method for 16-bit medical imaging volumes. The aim is to train a neural network (NN) as a 3D data predictor, which minimizes the differences with the original data values and to compress those residuals using arithmetic coding. We evaluate the compression performance of our proposed models to state-of-the-art lossless compression methods, which shows that our approach accomplishes a higher compression ratio in comparison to JPEG-LS, JPEG2000, JP3D, and HEVC and generalizes well. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
2815 |
| End Page: |
2819 |