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Conference Paper/Proceeding/Abstract 909 views 311 downloads

Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling

Omniah Nagoor, J. Whittle, Jingjing Deng, Benjamin Mora Orcid Logo, Mark Jones Orcid Logo

2020 IEEE International Conference on Image Processing (ICIP), Pages: 2815 - 2819

Swansea University Authors: Omniah Nagoor, Jingjing Deng, Benjamin Mora Orcid Logo, Mark Jones Orcid Logo

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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...

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Published in: 2020 IEEE International Conference on Image Processing (ICIP)
ISBN: 978-1-7281-6396-3 9781728163956
ISSN: 1522-4880 2381-8549
Published: IEEE 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55355
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first_indexed 2020-10-06T15:29:12Z
last_indexed 2020-12-03T04:10:26Z
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spelling 2020-12-02T15:13:27.2753404 v2 55355 2020-10-06 Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling ad8a0ed9b747350e0d626fe4398a9fe0 Omniah Nagoor Omniah Nagoor true false 6f6d01d585363d6dc1622640bb4fcb3f Jingjing Deng Jingjing Deng true false 557f93dfae240600e5bd4398bf203821 0000-0002-2945-3519 Benjamin Mora Benjamin Mora true false 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2020-10-06 SCS 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. Conference Paper/Proceeding/Abstract 2020 IEEE International Conference on Image Processing (ICIP) 2815 2819 IEEE 978-1-7281-6396-3 9781728163956 1522-4880 2381-8549 1 10 2020 2020-10-01 10.1109/icip40778.2020.9191031 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-12-02T15:13:27.2753404 2020-10-06T16:23:30.1094053 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Omniah Nagoor 1 J. Whittle 2 Jingjing Deng 3 Benjamin Mora 0000-0002-2945-3519 4 Mark Jones 0000-0001-8991-1190 5 55355__18349__c897033bda6149a89835ac643f9f4d26.pdf 2020_ICIP.pdf 2020-10-06T16:31:51.1740491 Output 206870 application/pdf Accepted Manuscript true true eng
title Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
spellingShingle Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
Omniah Nagoor
Jingjing Deng
Benjamin Mora
Mark Jones
title_short Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
title_full Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
title_fullStr Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
title_full_unstemmed Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
title_sort Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling
author_id_str_mv ad8a0ed9b747350e0d626fe4398a9fe0
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author_id_fullname_str_mv ad8a0ed9b747350e0d626fe4398a9fe0_***_Omniah Nagoor
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing Deng
557f93dfae240600e5bd4398bf203821_***_Benjamin Mora
2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
author Omniah Nagoor
Jingjing Deng
Benjamin Mora
Mark Jones
author2 Omniah Nagoor
J. Whittle
Jingjing Deng
Benjamin Mora
Mark Jones
format Conference Paper/Proceeding/Abstract
container_title 2020 IEEE International Conference on Image Processing (ICIP)
container_start_page 2815
publishDate 2020
institution Swansea University
isbn 978-1-7281-6396-3
9781728163956
issn 1522-4880
2381-8549
doi_str_mv 10.1109/icip40778.2020.9191031
publisher IEEE
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description 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.
published_date 2020-10-01T04:09:30Z
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score 11.037581

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