Wavelet coding of volumetric medical datasets

Schelkens, Peter; Munteanu, Adrian; Barbarien, Joeri; Galca, Mihnea; Giro-Nieto, X.; Cornelis, Jan

doi:10.1109/tmi.2003.809582

Cited by 151 publications

(85 citation statements)

References 39 publications

(77 reference statements)

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“…Selective data to access can be improved by splitting image into regions corresponding objects. A scheme based on the three-dimensional (3-D) discrete cosine transform (DCT) has been proposed for volumetric data coding (Schelkens, et al, 2003). These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications.…”

Section: Wavelet Codingmentioning

confidence: 99%

Medical Images Compression Using Modified SPIHT Algorithm and Multiwavelets Transformation

Al-Sammaraie¹

2011

CIS

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Recently, the wavelet transform has emerged as a cutting edge technology within the field of image compression research. Wavelet methods involve overlapping transforms with varying-length basis functions. This overlapping nature of the transform alleviates blocking artifacts, while the multi-resolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties which are needed for better performance in compression. New class of wavelets called 'Multiwavelets' which posses more than one scaling filters overcomes this problem. The objective of this paper is to develop an efficient compression scheme and to obtain better quality and higher compression ratio through multiwavelet transform and embedded coding of multiwavelet coefficients through Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm. A comparison of the best known multiwavelets is made to the best known scalar wavelets. An adaptive image-coding algorithm for compression of medical images in the wavelet domain is presented. Both quantitative and qualitative measures of performance are examined for Medical images. The objective (based on PSNR) and subjective (perceived image quality) results of these simulations are presented.

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Section: Wavelet Codingmentioning

confidence: 99%

Medical Images Compression Using Modified SPIHT Algorithm and Multiwavelets Transformation

Al-Sammaraie¹

2011

CIS

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“…The quantizer indices for each description are subsequently entropy coded. In our instantiation of the proposed architecture, we use the quadtree-limited (QT-L) coding algorithm of [27], [28]. QT-L efficiently exploits the intraband dependencies between the wavelet coefficients and provides compression performance competitive to JPEG2000.…”

Section: B Multiple Description Mctf-based Architecturementioning

confidence: 99%

Embedded Multiple Description Coding of Video

Verdicchio

Munteanu

Gavrilescu

et al. 2006

IEEE Trans. on Image Process.

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Abstract-Real-time delivery of video over best-effort errorprone packet networks requires scalable erasure-resilient compression systems in order to 1) meet the users' requirements in terms of quality, resolution, and frame-rate; 2) dynamically adapt the rate to the available channel capacity; and 3) provide robustness to data losses, as retransmission is often impractical. Furthermore, the employed erasure-resilience mechanisms should be scalable in order to adapt the degree of resiliency against transmission errors to the varying channel conditions. Driven by these constraints, we propose in this paper a novel design for scalable erasure-resilient video coding that couples the compression efficiency of the open-loop architecture with the robustness provided by multiple description coding. In our approach, scalability and packet-erasure resilience are jointly provided via embedded multiple description scalar quantization. Furthermore, a novel channel-aware rate-allocation technique is proposed that allows for shaping on-the-fly the output bit rate and the degree of resiliency without resorting to channel coding. As a result, robustness to data losses is traded for better visual quality when transmission occurs over reliable channels, while erasure resilience is introduced when noisy links are involved. Numerical results clearly demonstrate the advantages of the proposed approach over equivalent codec instantiations employing 1) no erasure-resilience mechanisms, 2) erasure-resilience with nonscalable redundancy, or 3) data-partitioning principles.Index Terms-Multiple description coding, packet erasure resilience, scalable video coding.

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“…However, the recent 3-D compression schemes for medical images [3]- [7] provide important functionalities like region of interest coding and progressive transmission of images. The schemes in [4] and [5] also provide additional functionality of decoding 2-D images or any objects of interest from the 3-D encoded images.…”

Section: E Future Workmentioning

confidence: 99%

“…We also propose an algorithm to avoid the classical aperture problem [19] that exists in regions of the image that have strongly oriented intensity gradients (edges) in both uniform and adaptive mesh-based schemes. Recent lossless still image compression schemes such as [3]- [7], [12]- [17] successfully employ context-based entropy coding methods. By choosing proper contexts, one can model the sources efficiently and reduce the entropy of the symbols to be coded.…”

mentioning

confidence: 99%