&lt;title&gt;Joint compression-segmentation of functional MRI data sets&lt;/title&gt;

Zhang, Ning; Wu, Mo; Forchhammer, Søren; Wu, Xiaolin

doi:10.1117/12.594677

Cited by 5 publications

(2 citation statements)

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“…The proposed system needs to compress the image in a lossless manner, the important information extracted from the whole brain image. A segmentation algorithm is used to extract the brain mass alone and eliminate other parts using threshold pixel values [15,16].…”

Section: Roi Extractionmentioning

confidence: 99%

Lossless Compression on MRI Images Using SWT

Anusuya¹,

Raghavan²,

Kavitha³

2014

J Digit Imaging

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Medical image compression is one of the growing research fields in biomedical applications. Most medical images need to be compressed using lossless compression as each pixel information is valuable. With the wide pervasiveness of medical imaging applications in health-care settings and the increased interest in telemedicine technologies, it has become essential to reduce both storage and transmission bandwidth requirements needed for archival and communication of related data, preferably by employing lossless compression methods. Furthermore, providing random access as well as resolution and quality scalability to the compressed data has become of great utility. Random access refers to the ability to decode any section of the compressed image without having to decode the entire data set. The system proposes to implement a lossless codec using an entropy coder. 3D medical images are decomposed into 2D slices and subjected to 2D-stationary wavelet transform (SWT). The decimated coefficients are compressed in parallel using embedded block coding with optimized truncation of the embedded bit stream. These bit streams are decoded and reconstructed using inverse SWT. Finally, the compression ratio (CR) is evaluated to prove the efficiency of the proposal. As an enhancement, the proposed system concentrates on minimizing the computation time by introducing parallel computing on the arithmetic coding stage as it deals with multiple subslices.

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Section: Roi Extractionmentioning

confidence: 99%

Lossless Compression on MRI Images Using SWT

Anusuya¹,

Raghavan²,

Kavitha³

2014

J Digit Imaging

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“…We code each code-cube independently using a modified EBCOT with 3-D contexts that exploit inter-slice correlations. Coding wavelet coefficients by extending 2-D context modeling to 3-D has been extensively used to improve coding efficiency [1], [2], [20], [21]. Here, we propose a 3-D context model, based on the four coding passes previously discussed, that incorporates information from the immediate horizontal, vertical, diagonal and temporal neighbors of sample located in slices , and , as illustrated in Fig.…”

Section: A Modified Ebcotmentioning

confidence: 99%

3-D Scalable Medical Image Compression With Optimized Volume of Interest Coding

Sánchez

Abugharbieh

Nasiopoulos

2010

IEEE Trans. Med. Imaging

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Abstract-We present a novel 3-D scalable compression method for medical images with optimized volume of interest (VOI) coding. The method is presented within the framework of interactive telemedicine applications, where different remote clients may access the compressed 3-D medical imaging data stored on a central server and request the transmission of different VOIs from an initial lossy to a final lossless representation. The method employs the 3-D integer wavelet transform and a modified EBCOT with 3-D contexts to create a scalable bit-stream. Optimized VOI coding is attained by an optimization technique that reorders the output bit-stream after encoding, so that those bits belonging to a VOI are decoded at the highest quality possible at any bit-rate, while allowing for the decoding of background information with peripherally increasing quality around the VOI. The bit-stream reordering procedure is based on a weighting model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. The background information with peripherally increasing quality around the VOI allows for placement of the VOI into the context of the 3-D image. Performance evaluations based on real 3-D medical imaging data showed that the proposed method achieves a higher reconstruction quality, in terms of the peak signal-to-noise ratio, than that achieved by 3D-JPEG2000 with VOI coding, when using the MAXSHIFT and general scaling-based methods.Index Terms-Embedded block coding with optimized truncation (EBCOT), medical image compression, scalable compression, volume of interest coding, 3D-JPEG2000.

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An Efficient Lossless Compression Algorithm for fMRI Data Volume

Zhang

2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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This paper presents an efficient lossless compression scheme designed for the fMRI data volume. An fMRI time series is composed of trend, noise and external stimulus response, which have different characteristics in wavelet domain. Reversible integer wavelet transform is applied to the fMRI time series. The smooth trend signals are extracted from the low frequency wavelet bands and they are coded by a lossy compression scheme. The lossy compression residual, noise, and other components are inversely transformed into time domain followed by adaptive clustering and then lossless coding. The experimental results show that the proposed method outperforms many existing schemes.

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<title>Joint compression-segmentation of functional MRI data sets</title>

Cited by 5 publications

References 0 publications

Lossless Compression on MRI Images Using SWT

Lossless Compression on MRI Images Using SWT

3-D Scalable Medical Image Compression With Optimized Volume of Interest Coding

An Efficient Lossless Compression Algorithm for fMRI Data Volume

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