Lossless Compression of Multidimensional Medical Images for Augmented Reality Applications
- 3.8k Downloads
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.
KeywordsMultidimensional medical images compression Multidimensional medical images coding Multidimensional data compression
Unable to display preview. Download preview PDF.
- 6.De Paolis, L.T., Pulimeno, M., Aloisio, G.: Advanced Visualization and Interaction Systems for Surgical Pre-operative Planning. CIT 18(4) (2010)Google Scholar
- 8.fMRI Wikipedia English Page. http://en.wikipedia.org/wiki/Fmri (accessed on July 2014)
- 9.Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd ed. Baltimore. MD: The Johns Hopkins Univ. Press (1996)Google Scholar
- 10.Knoll, B., De Freitas, N.: A Machine Learning Perspective on Predictive Coding with PAQ8. Data Compression Conference (DCC) 24(8), 377–386 (2012)Google Scholar
- 14.OpenfMRI Site. https://openfmri.org (accessed on July 2014)
- 15.Pizzolante, R., Carpentieri, B.: Lossless, low-complexity, compression of three-dimensional volumetric medical images via linear prediction. Digital Signal Processing (DSP), 1–6 (July 1-3, 2013)Google Scholar
- 18.Salomon, D., Motta, G.: Handbook of Data Compression, 5 edn. Springer (2010) ISBN: 978-1-84882-902-2Google Scholar