Volumetric Medical Image Coding: An Object-Based, Lossy-to-Lossless and Fully Scalable Approach

Abstract

• In this paper, an object-based, highly scalable, lossy-to-lossless 3D wavelet coding approach for volumetric
  medical image data (e.g. MR and CT) is proposed. The new method, called 3DOBHS-SPIHT, is based on the well
  known set partitioning in hierarchical trees (SPIHT) algorithm and supports both quality and resolution scalability.
  The 3D input data is grouped into groups of slices (GOS) and each GOS is encoded and decoded as a separate unit.
  The symmetric tree definition of the original 3DSPIHT is improved by introducing a new asymmetric tree structure.
  While preserving compression efficiency, the new tree structure allows for a small size of each GOS which not only
  reduces memory consumption during the encoding and decoding processes but also facilitates more efficient random
  access to certain segments of slices. To achieve more compression efficiency, the algorithm only encodes the main
  object of interest in each 3D data set, which can have any arbitrary shape, and ignores the unnecessary background.
  The experimental results on some MR data sets show the good performance of the 3DOBHS-SPIHT algorithm for
  multiresolution lossy-to-lossless coding. Compression efficiency, full scalability, and object-based features of the
  proposed approach, beside its lossy-to-lossless coding support, make it a very attractive candidate for volumetric
  medical image information archiving and transmission applications.