Diagnostic Compression of Biomedical Volumes

In this work we deal with lossy compression of biomedical volumes. By force of circumstances, diagnostic compression is bound to a subjective judgment. However, with respect to the algorithms, there is a need to shape the coding methodology so as to highlight beyond compression three important factors: the medical data, the specic usage and the particular end-user. Biomedical volumes may have very dierent characteristics which derive from imaging modality, resolution and voxel aspect ratio. Moreover, volumes are usually viewed slice by slice on a lightbox, according to dierent cutting direction (typically one of the three voxel axes). We will see why and how these aspects impact on the choice of the coding algorithm and on a possible extension of 2D well known algorithms to more ecient 3D versions. Cross-correlation between reconstruction error and signal is a key aspect to keep into account; we suggest to apply a non uniform quantization to wavelet coefficients in order to reduce slice PSNR variation. Once a good neutral coding for a certain volume is obtained, non uniform quantization can also be made space variant in order to reach more objective quality on Volumes of Diagnostic Interest (VoDI), which in turns can determine the diagnostic quality of the entire data set

Cyclostationary error analysis and filter properties in a 3D wavelet coding framework

The reconstruction error due to quantization of wavelet subbands can be modeled as a cyclostationary process because of the linear periodically shift variant property of the inverse wavelet transform. For N-dimensional data, N-dimensional reconstruction error power cyclostationary patterns replicate on the data sample lattice. For audio and image coding applications this fact is of little practical interest since the decoded data is perceived in its wholeness, the error power oscillations on single data elements cannot be seen or heard and a global PSNR error measure is often used to represent the reconstruction quality. A different situation is the one of 3D data (static volumes or video sequences) coding, where decoded data are usually visualized by plane sections and the reconstruction error power is commonly measured by a PSNR[n] sequence, with n representing either a spatial slicing plane (for volumetric data) or the temporal reference frame (for video). In this case, the cyclostationary oscillations on single data elements lead to a global PSNR[n] oscillation and this effect may become a relevant concern. In this paper we study and describe the above phenomena and evaluate their relevance in concrete coding applications. Our analysis is entirely carried out in the original signal domain and can easily be extended to more than three dimensions. We associate the oscillation pattern with the wavelet filter properties in a polyphase framework and we show that a substantial reduction of the oscillation amplitudes can be achieved under a proper selection of the basis functions. Our quantitative model is initially made under high-resolution conditions and then qualitatively extended to all coding rates for the wide family of bit-plane quantization-based coding techniques. Finally, we experimentally validate the proposed models and we perform a subjective evaluation of the visual relevance of the PSNR[n] fluctuations in the cases of medical volumes and video coding