Medical Data Compression via Component Segmentation

The patent segments the multicomponent medical raw data into multiple independent component datasets (e.g., bone, soft tissue, air components in CT; different receiver channels in MRI). Each component dataset is compressed separately using conventional compression methods, rather than compressing all components together as a single large dataset. This segmentation enables parallel processing of multiple smaller datasets, significantly improving compression speed while achieving efficient overall compression ratios.

The decoded compressed data is organized into separate component datasets corresponding to different anatomical or functional components. Each component dataset is decoded independently and then synthesized to reconstruct the complete medical image or dataset. This segmented approach reduces the computational complexity of decoding by avoiding the need to process the entire multicomponent dataset simultaneously, thereby improving decoding efficiency.

The patent divides the complex multicomponent medical data into multiple simpler component datasets that can be processed using standard compression algorithms. Each component is identified and separated based on its unique characteristics (e.g., density ranges in CT, signal frequencies in MRI), allowing the use of optimized compression parameters for each component type. This approach reduces overall processing complexity while achieving superior storage efficiency compared to treating all data uniformly.

The patent compresses multicomponent medical raw data by segmenting it into multiple component datasets, applying compression algorithms to each component separately. This reduces the overall data volume while preserving the integrity and completeness of all medical information. The compressed component datasets can then be transmitted more efficiently, achieving both fast transmission speeds and complete data transfer.