4D Medical Data Compression Architecture - Zagar, Martin
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This work presents a novel framework for 4D medical data compression architecture. This framework is based on different procedures and algorithms that detect temporal and spatial redundancies in recorded MRI volumes. Integration of segmentation, block matching and motion field prediction along with expert knowledge are incorporated to reduce temporal redundancy. Spatial analysis is done through the extension of wavelet transformations to three dimensions. With the combination of temporal and spatial redundancies removal, very high compression ratio is achieved. The suggested data compression…mehr

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Produktbeschreibung
This work presents a novel framework for 4D medical data compression architecture. This framework is based on different procedures and algorithms that detect temporal and spatial redundancies in recorded MRI volumes. Integration of segmentation, block matching and motion field prediction along with expert knowledge are incorporated to reduce temporal redundancy. Spatial analysis is done through the extension of wavelet transformations to three dimensions. With the combination of temporal and spatial redundancies removal, very high compression ratio is achieved. The suggested data compression architecture can be implemented in telemedicine to improve the quality of service. Each part of the system architecture is implemented independently and can be used for other approaches such as entertainment applications and other new applications that use 4D data.
Autorenporträt
Martin ¿agar, PhD is affiliated with the University of Zagreb, Faculty of Electrical Engineering and Computing. His research interests include telemedicine applications and multimedia processing and architectures.