Decomposition of 3D medical image based on Fast and Adaptive Bidimensional Empirical Mode Decomposition

Abstract

Three-dimensional (3D) imaging and display have been subjects of much research due to their diverse benefits and applications.This paper presents a new approach for decomposing the three-dimensional medical images using Bidimensional Empirical Mode Decomposition (BEMD).The BEMD is an extension of the Empirical Mode Decomposition (EMD), which can decompose non-linear and non-stationary signals into basis functions called the Intrinsic Mode Functions (IMFs).IMFs are monocomponent functions that have well defined instantaneous frequencies.This decomposition, obtained by a process known as sifting process, allows extracting the structures at different scales and spatial frequencies with modulation in amplitudes and frequency.BEMD decomposes an image into bidimensional BIMFs.This paper suggests a simple, but effective, method for decomposing a three-dimensional medical image into basis function.This approach is neither parametric nor data driven, which means it does not depend on a priori basis set.Moreover, it preserves the totality of information in term of the quality of the reconstructed 3D image.The performance of this approach, using the BEMD, is approved with some medical images.