Evaluating Lossy and Lossless Compression for DICOM Medical Files

Digital Imaging and Communications in Medicine (DICOM) is a widely used standard for handling, storing, and sharing medical images. However, the large file sizes associated with DICOM data pose challenges for storage and data transfer. Data reduction helps mitigate these challenges by reducing the size of the data while maintaining its integrity. This paper examines various compression methods to reduce the size of DICOM files. We evaluate 5 lossless and 4 lossy compressors on DICOM data. This study aims to compare and evaluate the performance of these compressors. By analyzing each compressor’s compression efficiency and produced image fidelity, this research seeks to determine the most effective compression strategy. Results show SZ3 is able to achieve 183.74× with error bound 1e<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup> and ZFP received compression bandwidth 303.82 MB/s while error bound is 1e<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup>.

Authors

Institutions

Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825078
Primary Topic
Advanced Data Compression Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating Lossy and Lossless Compression for DICOM Medical Files

Jon C. Calhoun, Yizhe Yang, Carson D. Sisk
Advanced Data Compression Techniques
article

Evaluating Lossy and Lossless Compression for DICOM Medical Files

Jon C. Calhoun, Yizhe Yang, Carson D. Sisk
article en

Abstract

Digital Imaging and Communications in Medicine (DICOM) is a widely used standard for handling, storing, and sharing medical images. However, the large file sizes associated with DICOM data pose challenges for storage and data transfer. Data reduction helps mitigate these challenges by reducing the size of the data while maintaining its integrity. This paper examines various compression methods to reduce the size of DICOM files. We evaluate 5 lossless and 4 lossy compressors on DICOM data. This study aims to compare and evaluate the performance of these compressors. By analyzing each compressor’s compression efficiency and produced image fidelity, this research seeks to determine the most effective compression strategy. Results show SZ3 is able to achieve 183.74× with error bound 1e<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup> and ZFP received compression bandwidth 303.82 MB/s while error bound is 1e<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup>.

Clemson University (US)
National Science Foundation, Advanced Research Projects Agency
Openalex Percentile: Top 74%
Advanced Data Compression Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.