A data compression method for image time series

Cohen, Mark S.

doi:10.1002/1097-0193(200101)12:1<20::aid-hbm20>3.0.co;2-n

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Previous works also explored the redundancies in the temporal dimension of medical image data for compression purposes. The work presented in [ 20 ] calculates the differences between two contiguous images from a medical image time series and store these differences using eight bits when this is possible. When this difference cannot be expressed using eight bits, the original 16 bits are used.…”

Section: Discussionmentioning

confidence: 99%

Dynamic CT perfusion image data compression for efficient parallel processing

Barros

Olabarriaga

Borst

et al. 2015

Med Biol Eng Comput

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The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes transfers to cloud infrastructures time-consuming and therefore not suitable in acute situations. To reduce this transfer time, this work proposes a fast and lossless compression algorithm for CTP data. The algorithm exploits redundancies in the temporal dimension and keeps random read-only access to the image elements directly from the compressed data on the GPU. To the best of our knowledge, this is the first work to present a GPU-ready method for medical image compression with random access to the image elements from the compressed data.

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Section: Discussionmentioning

confidence: 99%

Dynamic CT perfusion image data compression for efficient parallel processing

Barros

Olabarriaga

Borst

et al. 2015

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…In the case of lossless compression, SmallTime [4] performs a very fast by rather inefficient fMRI compression by simply taking difference images between adjacent recordings and then storing the typically 8-bit difference image in contrast to the 16-bit recorded image. Applying an international compression standard is a straightforward way.…”

Section: Fabian J Theismentioning

confidence: 99%

A Fast Predictive Lossless Coder for fMRI Data Sets

Tanaka

Murakami

Theis

2006

2006 International Conference on Image Processing

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We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved in this popular biomedical application necessitate fast and efficient compression methods. We propose to use 3D prediction, temporal decorrelation and entropy coding with context modeling for encoding the fMRI scans after preprocessing with the region-of-interest (ROI) masking. The proposed algorithm is conceptually simple and can achieve fast implementation and efficient coding performance. We illustrate computer simulations to show advantages over conventional coding methods.

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A Fast and Efficient Method for Compressing fMRI Data Sets

Theis

Tanaka

2005

Lecture Notes in Computer Science

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A data compression method for image time series

Cited by 5 publications

References 7 publications

Dynamic CT perfusion image data compression for efficient parallel processing

Dynamic CT perfusion image data compression for efficient parallel processing

A Fast Predictive Lossless Coder for fMRI Data Sets

A Fast and Efficient Method for Compressing fMRI Data Sets

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