A fast and efficient method for compressing fMRI data sets

Authors:
Fabian J. Theis;Toshihisa Tanaka
Affiliations:
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan and Institute of Biophysics, University of Regensburg, Regensburg, Germany;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 2
Cited 1

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JPEG 2000: Image Compression Fundamentals, Standards and Practice
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

IEEE Transactions on Image Processing

Novel lossless fMRI image compression based on motion compensation and customized entropy coding

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics

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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.