Visualization of S transform data using principal-component analysis

摘要

The present invention relates to a method for visualizing ST data based on principal component analysis. ST data indicative of a plurality of local S spectra, each local S spectrum corresponding to an image point of an image of an object are received. In a first step principal component axes of each local S spectrum are determined. This step is followed by the determination of a collapsed local S spectrum by projecting a magnitude of the local S spectrum onto at least one of its principal component axes, thus reducing the dimensionality of the S spectrum. After determining a weight function capable of distinguishing frequency components within a frequency band a texture map for display is generated by calculating a scalar value from each principal component of the collapsed S spectrum using the weight function and assigning the scalar value to a corresponding position with respect to the image. The visualization method according to the invention is a highly beneficial tool for image analysis substantially retaining local frequency information but not requiring prior knowledge of frequency content of an image. Employment of the visualization method according to the invention is highly beneficial, for example, for motion artifact suppression in MRI image data, texture analysis and disease specific tissue segmentation.