We propose and evaluate a number of novel improvements to the mesh-basedcoding scheme for 3-d brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing ...
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We propose and evaluate a number of novel improvements to the mesh-basedcoding scheme for 3-d brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image;2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices;3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible;and 4) context-based entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-d) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-d brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity.
We propose a fully three-dimensional (3-d) object-basedcoding 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 ...
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We propose a fully three-dimensional (3-d) object-basedcoding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-ddiscrete 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.
In this paper, we propose a lossless compression algorithm for three-dimensional (3-d) binary voxel surfaces, based on the pattern code representation (PCR). In PCR, a voxel surface is represented by a series of patte...
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In this paper, we propose a lossless compression algorithm for three-dimensional (3-d) binary voxel surfaces, based on the pattern code representation (PCR). In PCR, a voxel surface is represented by a series of pattern codes. The pattern of a voxel v is defined as the 3 x 3 x 3 array of voxels, centered on v. Therefore, the pattern code for v informs of the local shape of the voxel surface around v. The proposed algorithm can achieve the coding gain, since the patterns of adjacent voxels are highly correlated to each other. The performance of the proposed algorithm is evaluated using various voxel surfaces, which are scan-converted from triangular mesh models. It is shown that the proposed algorithm requires only 0.5similar to1 bits per black voxel (bpbv) to store or transmit the voxel surfaces.
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