Parametric Visualization of High Resolution Correlated Multi-spectral Features Using PCA

An imaging mass spectrometer is an analytical instrument that can determine the spatial distribution of chemical compounds on complex surfaces. The output of the device is a multi-spectral datacube; a three-dimensional (3D) dataset in which the xy-dimension represents the surface position and the z-dimension represents the mass spectral distribution. Analysts try to discover correlations in spectral profiles and spatial distributions inside a datacube. New technological developments allow mass spectrometric imaging on a higher spatial and spectral resolution. In this paper we present a parametric visualization technique which allows an analyst to examine spectral and spatially correlated patterns on the highest possible resolution. Principal component analysis (PCA) is used to e the datacube into several discriminating components. We represent these extracted features as abstract geometric shapes and use three parameters to allow for data exploration. The first parameter thresholds th...

• Type: Conference
• Year: 2007
• Where: VISSYM
• Authors: Alexander Broersen, Robert van Liere, Ron M. A. Heeren
• Keywords: Extracted Features, Spatial Distribution, Spectral Resolution, VISSYM 2007, Visualization