Researchers in Slovenia have proposed a new algorithm for the lossless compression of volumetric data sets, a finding that may allow CT and MR modalities to compress voxel data during acquisition. The new method builds a quadtree representation of each slice (Comput Med Imaging Graph 2005;29(4):305-312).
Researchers in Slovenia have proposed a new algorithm for the lossless compression of volumetric data sets, a finding that may allow CT and MR modalities to compress voxel data during acquisition. The new method builds a quadtree representation of each slice (Comput Med Imaging Graph 2005;29(4):305-312).
Most existing lossy algorithms use octree encoding for compression, which does not exploit the coherence between neighboring slices but instead creates a common voxel space. This requires large onboard memory, according to researchers at the University of Maribor.
The researchers instead use a different compression approach, processing two consecutive slices at a time. Some properties of voxel data sets, such as homogeneity and coherence, can be used more efficiently to produce better compression ratios.
The quadtree method is similar to the interframe-based digital video compression method used by MPEG-2, in that it exploits similarities between neighboring slices. In contrast to MPEG-2, which forms a set of equally sized blocks of pixels (macroblocks) and predicts where they would move in the next frame, the group's algorithm uses macroblocks of different sizes and does not make any prediction. The positions and sizes of the macroblocks are coded by a quadtree.
Emerging AI Mammography Model May Enhance Clarity for Initial BI-RADS 3 and 4 Classifications
May 21st 2025In a study involving over 12,000 Asian women, researchers found that an artificial intelligence (AI) model converted over 83 percent of false positives in patients with initial BI-RADS 3 and 4 assessments into benign BI-RADS categories.