Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update

TL;DR

This paper introduces a novel processing order in motion compensated wavelet lifting for lossless dynamic CT data compression, incorporating denoising filters to improve compression efficiency while maintaining visual quality.

Contribution

It proposes a new sequence of motion compensation and denoising steps, including a second denoising filter, to enhance lossless compression of medical CT data.

Findings

File size reduced by up to 4.4%

Visual quality of lowpass subband maintained

Improved compression ratio with denoising filters

Abstract

Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the lowpass subband, motion compensation can be incorporated into the lifting structure, but leads to inferior compression results at the same time. Prior work has shown that a denoising filter in the update step can improve the compression ratio. In this paper, we present a new processing order of motion compensation and denoising in the update step and additionally introduce a second denoising filter in the prediction step. This allows for reducing the overall file size by up to 4.4%, while the visual quality of the lowpass subband is kept nearly constant.