The use of contextual information in the reversible compression of medical images

Ramabadran, Tenkasi V.; Chen, K.

doi:10.1109/42.141642

Cited by 68 publications

(25 citation statements)

References 22 publications

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“…On the other hand, lossy compression can achieve higher compression ratios. However, the original images can only be reconstructed approximately from compressed data, though the differences may not be distinguishable by the human visual system [5], [25], [31], [32]. The challenge in the development of a practical image compression scheme for dynamic medical images is the development of compression algorithms that are lossless for diagnostic purposes, i.e., make no difference to doctors, qualitative and quantitative assessment, yet attain high compression ratios to reduce storage, transmission, and processing burdens.…”

Section: Data Compression Storage and Managementmentioning

confidence: 99%

Information technology applications in biomedical functional imaging

Feng

1999

IEEE Trans. Inform. Technol. Biomed.

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Abstract-In parallel with rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing, and communication difficult. Using the minimum amount of data to represent the required information, developing fast algorithms to process the data, organizing the data in such a way as to facilitate information management, and extracting the maximum amount of useful information from the recorded data have become important research tasks in biomedical information technology. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and, recently, the Center for Multimedia Signal Processing have conducted systematic studies on these topics. Some of the results relating to functional imaging data acquisition, compression, storage, management, processing, modeling, and simulation are briefly reported in this paper.Index Terms-Biomedical functional imaging, information technology.

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Section: Data Compression Storage and Managementmentioning

confidence: 99%

Information technology applications in biomedical functional imaging

Feng

1999

IEEE Trans. Inform. Technol. Biomed.

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“…C(X) = ( w;ww; nw;n; nn; ne; 2n , nn; 2w , ww): (1) where each of the elements is the pixel in that direction (north, south, ...) with respect to the pixel being encoded. The two values 2n , nn and 2w , ww do not correspond to actual pixel values.…”

Section: Context Classification and Adaptive Predictionmentioning

confidence: 99%

“…Use of "context" [1] information to provide adaptivity has demonstrated significant improvements in lossless compression results. However, many of these techniques are based on heuristics and often do not fully exploit the gains offered by adaptive coding.…”

Section: Introductionmentioning

confidence: 99%

A lossless image coder with context classification, adaptive prediction and adaptive entropy coding

Golchin

Paliwal

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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In this paper, we combine a context classification scheme with adaptive prediction and entropy coding to produce an adaptive lossless image coder. In this coder, we maximize the benefits of adaptivity using both adaptive prediction and entropy coding. The adaptive prediction is closely tied with the classification of contexts within the image. These contexts are defined with respect to the local edge, texture or gradient characteristics as well as local activity within small blocks of the image. For each context an optimal predictor is found which is used for the prediction of all pixels belonging to that particular context. Once the predicted values have been removed from the original image, a clustering algorithm is used to design a separate, optimal entropy coding scheme for encoding the prediction residual. Blocks of residual pixels are classified into a finite number of classes and members of each class are encoded using the entropy coder designed for that particular class. The combination of these two powerful techniques produces some of the best lossless coding results reported so far.

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“…All lossless image compression methods [98,19,88] utilize variable-length coding, and sometimes quantization as a front stage of compression.…”

Section: Lossy Versus Lossless Compressionmentioning

confidence: 99%

Some new developments in image compression

Chen

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The use of contextual information in the reversible compression of medical images

Cited by 68 publications

References 22 publications

Information technology applications in biomedical functional imaging

Information technology applications in biomedical functional imaging

A lossless image coder with context classification, adaptive prediction and adaptive entropy coding

Some new developments in image compression

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