Lossless Compression of Multidimensional Medical Images for Augmented Reality Applications
Medical digital imaging technologies produce daily a huge amount of data (data obtained by magnetic resonance, computed tomography and ultrasound examinations, functional resonance magnetic acquisitions, etc.), which is generally stored in ad-hoc repositories or it is transmitted to other entities,...
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Published in | Augmented and Virtual Reality pp. 124 - 136 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
2014
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3319139681 9783319139685 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-319-13969-2_10 |
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Summary: | Medical digital imaging technologies produce daily a huge amount of data (data obtained by magnetic resonance, computed tomography and ultrasound examinations, functional resonance magnetic acquisitions, etc.), which is generally stored in ad-hoc repositories or it is transmitted to other entities, such as research centers, hospital structures, etc.. These data need efficient compression, in order to optimize memory space and transmission costs. In this work, we introduce an efficient lossless algorithm that can be used for the compression of volumetric multidimensional medical image sequences. This approach can be also used, in conjunction with Augmented Reality techniques, to save in a database or to transmit on a communication line the outcomes of surgical decisions or medical applications. We experimentally test our approach on a test set of 3-D computed tomography (CT), 3-D magnetic resonance (MR) images, and of 5-D functional Magnetic Resonance Images (fMRI). The achieved results outperform the other state-of-the-art approaches. |
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ISBN: | 3319139681 9783319139685 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-319-13969-2_10 |