Scalable Compression of 3-D Medical Image Data Using EBCOT with Volume of Interest Coding
DOI:
https://doi.org/10.51983/ajcst-2012.1.1.1673Keywords:
Embedded block coding with optimized truncation (EBCOT), Integer wavelet transform, medical image compression, scalable compression, volume of interest coding, 3D-JPEG2000Abstract
Volumetric medical image data usually require a vast amount of resources for storage and transmission. With the wide pervasiveness of medical imaging applications in healthcare 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 to avoid any negative effects of lossy compression on image quality and diagnostic capabilities. The main objective is to present a 3-D medical image compression method with scalability properties, by quality and resolution up to lossless reconstruction and optimized Volume of Interest (VOI) coding at any bit-rate. The proposed method named as “EBCOT with VOI coding” employs a 3-D integer wavelet transform (3D-IWT) and a modified EBCOT with 3-D contexts to compress the 3-D medical imaging data into a layered bit-stream that is scalable by quality and resolution. VOI coding capabilities are attained after compression by employing a bit-stream reordering procedure, which is based on a weight assignment model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. Optimized VOI coding at any bit-rate is attained by an optimization technique that maximizes the reconstruction quality of the VOI, while allowing for the decoding of background information with peripherally increasing quality around the VOI. 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.
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