Study on multi-description coding for ROI medical image based on EBCOT

Alin Hou; Lihong Zhang; Dongcheng Shi; Guangming Cui; Kun Xu; Wen Zhou; Jie Liu

doi:10.1117/12.759577

29 November 2007 Study on multi-description coding for ROI medical image based on EBCOT

Alin Hou, Lihong Zhang, Dongcheng Shi, Guangming Cui, Kun Xu, Wen Zhou, Jie Liu

Proceedings Volume 6833, Electronic Imaging and Multimedia Technology V; 683335 (2007) https://doi.org/10.1117/12.759577
Event: Photonics Asia 2007, 2007, Beijing, China

Abstract

Embedded block coding with optimized truncation (EBCOT) with the wavelet shape of tree encoding structure is more flexible because it encodes each code block respectively by decomposing the subband into code blocks, so that the embedded code streams will come into being to support the mass classification, the hierarchical resolution and the random access. However the anti-missing performance via network of the algorithm is worse. The source signal has been divided into many code streams by multi-description coding (MDC) of image and video so that it will be transferred through the insecure transmission channel. Region of Interest (ROI) coding gives priority to the focus of doctor's interest generally occupies lesser part of entire medical image. In this paper, ROI coding, which combines MDC and EBCOT, has been done according to JPEG2000 ROI coding standard in medical images. The algorithm not only uses the hierarchical spatial resolution and random access to ROI of EBCOT, but also has improved the anti-missing performance via network, and formed robust code stream. The experimental results demonstrated that the coding method improved the system compression ratio without influence on the medical diagnosis.

Citation Download Citation

Alin Hou, Lihong Zhang, Dongcheng Shi, Guangming Cui, Kun Xu, Wen Zhou, and Jie Liu "Study on multi-description coding for ROI medical image based on EBCOT", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 683335 (29 November 2007); https://doi.org/10.1117/12.759577

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KEYWORDS

Image compression

Medical imaging

Image quality

Wavelets

Image processing

Computer programming

Image restoration

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