Imaging mass spectrometry is a technique to determine of which materials a small, physical sample is made. Current feature extraction techniques fail to extract certain small, high resolution characteristics from these multi-spectral datacubes. Causes are a low signal-to-noise ratio, the presence of dominant but uninteresting features, and the huge amount of variables in the dataset. In this paper, we present a zooming technique based on principal component analysis (PCA) to select regions in a datacube for enhanced feature extraction at the highest possible resolution. It enables the selection of spectral and spatial regions at a low resolution and recursively apply PCA to zoom in on interesting, correlated features. This approach is not based on complex and data-specific denoising algorithms. Moreover, it decreases execution time when additional filters have to be applied. The technique utilizes a higher signal-to-noise ratio in the data, without losing the high resolution characteristics. Less interesting and/or dominating features can be excluded in the spectral and spatial dimension. For these reasons, more features can be distinguished and in greater detail. Analysts can zoom into a feature of interest by increasing the resolution.

Additional Metadata
Publisher Bellingham: SPIE
Editor K. Börner , M.T. Gröhn , J. Park
Persistent URL dx.doi.org/10.1117/12.766450
Citation
Broersen, A, van Liere, R, & Heeren, R.M.A. (2008). Zooming in multi-spectral datacubes using PCA. In K Börner, M.T Gröhn, & J Park (Eds.), Visualization and data analysis 2008 : 28-29 January 2008, San Jose, California, USA. Bellingham: SPIE. doi:10.1117/12.766450