title: Lossy to lossless object-based coding of 3-D MRI data
abstract: We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature
keyphrases: lossless object-based coding 3-D MRI data diagnostic relevance volumetric data rate allocation 3-D discrete wavelet transform lifting steps scheme integer-to-integer values object-based inverse transform disjoint segments bitstream embedded zerotree coding EZW-3D multidimensional layered zero coding region boundaries filter length decomposition depth head magnetic resonance images lossy object-based coding NMZQ region of interest-based processing ROI-based processing