New technological advancements including multislice CT scanners and functional MRI, have dramatically increased the size and number of digital images generated by medical imaging departments. Despite the fact that the cost of storage is dropping, the savings are largely surpassed by the increasing volume of data being generated. While local area network bandwidth within a hospital is adequate for timely access to imaging data, efficiently moving the data between institutions requires wide area network bandwidth, which has a limited availability at a national level. A solution to address those issues is the use of lossy compression as long as there is no loss of relevant information. The goal of this study was to determine levels at which lossy compression can be confidently used in diagnostic imaging applications. In order to provide a fair assessment of existing compression tools, we tested and compared the two most commonly adopted DISCOM compression algorithms: JPEG and JPEG-2000. We conducted an extensive pan-Canadian evaluation of lossy compression applied to seven anatomical areas and five modalities using two recognized techniques: objective methods or diagnostic accuracy and subjective assessment based on Just Noticeable Difference. By incorporating both diagnostic accuracy and subjective evaluation techniques, enabled us to define a range of compression for each modality and body part tested. The results of our study suggest that at low levels of compression, there was no significant difference between the performance of lossy JPEG and lossy JPEG 2000, and that they are both appropriate to use for reporting on medical images. At higher levels, lossy JPEG proved to be more effective than JPEG 2000 in some cases, mainly neuro CT. More evaluation is required to assess the effect of compression on thin slice CT. We provide a table of recommended compression ratios for each modality and anatomical area investigated, to be integrated in the Canadian Association of Radiologists standard for the use of lossy compression in medical imaging.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3043739PMC
http://dx.doi.org/10.1007/s10278-008-9139-7DOI Listing

Publication Analysis

Top Keywords

lossy compression
16
compression
12
lossy jpeg
12
pan-canadian evaluation
8
compression ratios
8
medical imaging
8
area network
8
network bandwidth
8
levels lossy
8
diagnostic accuracy
8

Similar Publications

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy.

View Article and Find Full Text PDF

State-of-the-Art Trends in Data Compression: COMPROMISE Case Study.

Entropy (Basel)

November 2024

Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.

After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight.

View Article and Find Full Text PDF

Adaptive Compression and Reconstruction for Multidimensional Medical Image Data: A Hybrid Algorithm for Enhanced Image Quality.

J Imaging Inform Med

December 2024

Department of Computer Science and Engineering, College of Engineering, Anna University, Guindy, Chennai, Tamilnadu, India.

Spatial regions within images typically hold greater priority over adjacent areas, especially in the context of medical images (MI) where minute details can have significant clinical implications. This research addresses the challenge of compressing medical image dimensions without compromising critical information by proposing an adaptive compression algorithm. The algorithm integrates a modified image enhancement module, clustering-based segmentation, and a variety of lossless and lossy compression techniques.

View Article and Find Full Text PDF
Article Synopsis
  • Video-based point cloud compression (V-PCC) is a new MPEG standard that effectively compresses both static and dynamic point clouds with various levels of quality loss.
  • In scenarios where the original point cloud isn't available, it’s important to create reduced-reference quality metrics, which can evaluate visual quality without direct comparison to the original.
  • The study proposes a new metric called PCQAML, which uses a set of 19 selected features related to various aspects of point clouds and demonstrates superior performance against existing metrics in multiple statistical measures.
View Article and Find Full Text PDF
Article Synopsis
  • - This paper introduces a neural recording integrated circuit (IC) designed for brain-computer interfaces, allowing for high-bandwidth and single-cell resolution data compression during digitization to manage large amounts of data more efficiently.
  • - The IC reduces the output data rate by 146× by eliminating unnecessary baseline samples while still enabling the reconstruction of important neural signals, using a low-power design and an effective routing system.
  • - Fabricated in a compact 28-nm CMOS process, the IC features a 32 x 32 array with 1024 channels, achieving high energy efficiency and low noise levels, making it suitable for integration with high-density microelectrode arrays.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!