Abstract: A multi-dimensional empirical mode decomposition method is provided. The method can be applied in image texture analysis, such as medical image analysis. The method can adaptively decompose a three-dimensional image into a number of characteristic image layers and extract characteristic images showing more noticeable textures from the layers. The method uses the physical concept of field to perform the data mode decomposition to obtain envelope and tendency estimation of multi-dimensional data. The method can also be applied in time and frequency analysis of two-dimensional data or multi-channel data.

Abstract: A signal processing method for performing hierarchical empirical mode decomposition (H-EMD) and an apparatus therefor are provided. In an embodiment, when empirical mode decomposition is performed on an input signal, an artificial assisting signal and the input signal are combined to assist the search for extrema and frequency reduction is performed in each iteration to eliminate the artificial assisting signal and make mode decomposition convergent so as to avoid mode mixing. In addition, in an embodiment, a hierarchical decomposition method is provided to decompose the input signal into a fewer number of fundamental modes. For needs in application, one of the fundamental modes can be further decomposed to produce a number of supplementary modes. In an embodiment, the H-EMD with appropriate frequency reduction can result in modes substantially independent of the form or the way of envelopes and can be applied to decompose multi-dimensional signals.

Abstract: A multi-dimensional empirical mode decomposition method is provided. The method can be applied in image texture analysis, such as medical image analysis. The method can adaptively decompose a three-dimensional image into a number of characteristic image layers and extract characteristic images showing more noticeable textures from the layers. The method uses the physical concept of field to perform the data mode decomposition to obtain envelope and tendency estimation of multi-dimensional data. The method can also be applied in time and frequency analysis of two-dimensional data or multi-channel data.