3D neuro-anatomical images and other volumetric data sets are important in many scientific and biomedical fields. To achieve a high compression rate, near-lossless or lossless compression algorithm is applied which uses a Hilbert traversal to produce a data stream from the original image. An extremely fast linear DPCM is used. The linear DPCM takes the average of the previous two voxels' intensity to predict the current voxel's intensity. If near-lossless compression is desired, the prediction error can be uniformly quantized. For lossless mode, no action is taken on the prediction error. The prediction error is further encoded using Huffman code. In order to provide efficient data access, the source image is divided into blocks and indexed by an octree data structure. The points are binned using a novel binning method. All the error distributions that fall into the same bin are summed together to form a summed error distribution. Although our compression method is designed for performance-critical digital brain atlas applications, it would be suitable for other applications with fast data access.
['Rongkai Zhao', 'Michael Gabriel', 'Geneva G. Belford']
Fast near-lossless or lossless compression of large 3D neuro-anatomical images