Abstract
We present a new lossless compression method named FTTcoder, which compresses images and 3d sequences collected during a typical functional MRI experiment. The large data sets involved in this popular medical application necessitate novel compression algorithms to take into account the structure of the recorded data as well as the experimental conditions, which include the 4d recordings, the ...
Abstract
We present a new lossless compression method named FTTcoder, which compresses images and 3d sequences collected during a typical functional MRI experiment. The large data sets involved in this popular medical application necessitate novel compression algorithms to take into account the structure of the recorded data as well as the experimental conditions, which include the 4d recordings, the used stimulus protocol and marked regions of interest (ROI). We propose to use simple temporal transformations and entropy coding with context modeling to encode the 4d scans after preprocessing with the ROI masking. Experiments confirm the superior performance of FTTcoder in contrast to previously proposed algorithms both in terms of speed and compression.
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