Lossless compression for 3-D MRI data using reversible KLT

Yodchanan, W.

doi:10.1109/icalip.2008.4590113

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…The KLT has been used as a lossy spectral decorrelator to reduce spectral redundancy in multi-component image compression studies (e.g. hyperspectral imaging for remote sensing and magnetic resonance imaging (MRI) for medical applications (Yodchanan 2008)). KLT has also been applied to facial recognition (Nefian and Hayes 1998) and pattern finding in high-dimension data Quintiliano and Santa-Rosa (2003).…”

Section: The Integer Karhunen-loèvetransformmentioning

confidence: 99%

“…Spectraldecorrelation aims to reduce the spectral redundancy that exists between bands, whereas spatial decorrelation aims to reduce the spatial redundancy within a band. This schemehas been used widely in remote sensing (Qian and Fowler 2007;Blanes and Serra-Sagristà 2009;Yu et al 2009;Mat Noor et al 2010), as well as in medical applications (Yodchanan et al 2006a;Yodchanan et al 2006b;Yodchanan 2008)for three-dimensional (3D) medicaldata.…”

Section: Hyperspectral Image Compression: An Introductionmentioning

confidence: 99%

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Investigation into lossless hyperspectral image compression for satellite remote sensing

Noor

Vladimirova

2013

International Journal of Remote Sensing

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Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen-Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.

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Section: The Integer Karhunen-loèvetransformmentioning

confidence: 99%

Section: Hyperspectral Image Compression: An Introductionmentioning

confidence: 99%

Investigation into lossless hyperspectral image compression for satellite remote sensing

Noor

Vladimirova

2013

International Journal of Remote Sensing

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“…This makes MRI especially useful in the brain and cancer imaging [24]. An MRI scan can be used as an extremely accurate tool for disease detection throughout the body.…”

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confidence: 99%

“…Beside these references that have used 3D datasets, some works have been performed on the use of 3D predictor design such as that of [23] and [19]. The method that is presented in [24] is designed to use 1-D optimal transform, i.e. KLT, which is followed by an arbitrary lossless image coder, e.g.…”

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confidence: 99%

Use of symmetry in prediction-error field for lossless compression of 3D MRI images

Karimi

Samavi

Amraee

et al. 2014

Multimed Tools Appl

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Three dimensional MRI images which are powerful tools for diagnosis of many diseases require large storage space. A number of lossless compression schemes exist for this purpose. In this paper we propose a new approach for lossless compression of these images which exploits the inherent symmetry that exists in 3D MRI images. First, an efficient pixel prediction scheme is used to remove correlation between pixel values in an MRI image. Then a block matching routine is employed to take advantage of the symmetry within the prediction error image. Inter-slice correlations are eliminated using another block matching. Results of the proposed approach are compared with the existing standard compression techniques.

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“…(1) Apply techniques from 1D and 2D compression. For example, Yodchanan [80] use reversible KLT to remove redundancy to lossless compress 3D MRI data.…”

Section: D Data Compressionmentioning

confidence: 99%

LIDAR data classification and compression

Li¹

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Lossless compression for 3-D MRI data using reversible KLT

Cited by 10 publications

References 19 publications

Investigation into lossless hyperspectral image compression for satellite remote sensing

Investigation into lossless hyperspectral image compression for satellite remote sensing

Use of symmetry in prediction-error field for lossless compression of 3D MRI images

LIDAR data classification and compression

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