engleski

Data Compression as a Base for eHealth Interoperability: 3D FWT applied on Volumetric Neuroimages

In this chapter we have developed framework for construction of compression methods, built on wavelet bases in higher dimensions, and we applied it on experimental volumetric neuroimage. We have proofed the construction of orthonormal basis of and we have verified wavelet expressions for fast 3D wavelet transform. We applied Daubechies D2, D10, D20 and Cohen-Daubechies-Feauveau 5/3 wavelet bases and concluded that with Haar (D2) wavelet we can achieve very high compression ratio but with not enough accuracy of details. Compared to JP3D (i.e. CDF 5/3) D20 wavelet gives better compression ratio but it is slower and D10 has better performance with almost same execution time, so as a conclusion we propose usage of D10 wavelet base for usage in medical data compression as a Base for eHealth Interoperability.

Data Compression, eHealth Interoperability, 3D FWT

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