Izvorni znanstveni članak

Framework for 4D medical data compression

Martin Žagar ; Fakultet elektrotehnike i računarstva, Unska 3, 10000 Zagreb, Hrvatska
Mario Kovač ; Fakultet elektrotehnike i računarstva, Unska 3, 10000 Zagreb, Hrvatska
Daniel Hofman orcid.org/0000-0002-7154-0413 ; Fakultet elektrotehnike i računarstva, Unska 3, 10000 Zagreb, Hrvatska

Sažetak

This work presents a novel framework for four-dimensional (4D) medical data compression architecture. This framework is based on different procedures and algorithms that detect time and spatial (frequency) redundancy in recorded 4D medical data. Motion in time is analyzed through the motion fields that produce input parameters for the neural network used for motion estimation. Combination of segmentation, block matching and motion field prediction along with expert knowledge are incorporated to achieve better performance. Frequency analysis is done through an extension of one dimensional wavelet transformation to three dimensions. For still volume objects different wavelet packets with different filter banks can be constructed, providing a wide range of frequency analysis. With combination of removing temporal and spatial redundancies, very high compression ratio is achieved.

Ključne riječi

3D wavelet transformation; 4D medical data; block matching; motion field; temporal and frequency redundancies

Hrčak ID:

79164

URI

https://hrcak.srce.hr/79164

Datum izdavanja:

29.3.2012.

Podaci na drugim jezicima: hrvatski

Posjeta: 2.013 *